Patent Publication Number: US-11379512-B2

Title: Sentiment-based classification of media content

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/914,906, filed Jun. 29, 2020, which is a continuation of U.S. patent application Ser. No. 16/259,709, filed Jan. 28, 2019, which is a continuation of U.S. patent application Ser. No. 15/851,159, filed Dec. 21, 2017, which is a continuation of U.S. patent application Ser. No. 15/287,635, filed Oct. 6, 2016, which is a continuation of U.S. patent application Ser. No. 14/827,181, filed Aug. 14, 2015, which is a divisional of U.S. patent application Ser. No. 12/268,324, filed Nov. 10, 2008, each of which is hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates generally to browsing and searching videos and other media hosted at a website. In particular, embodiments of the present invention are directed toward providing browsing and searching of media based on sentiment expressed in comments associated with the media. 
     BACKGROUND 
     Various media hosting websites provide methods for users to comment on the media content hosted by the websites. For example, news publications, blogging sites, video and audio content providers typically provide a “comment” option. A visitor to the site who wishes to comment on a story or other posted media content can generally select a “comment” or “feedback” link, and post textual comments for other visitors of the site to view. 
     User comments can indicate user opinion or sentiment about the media content. For example, users may provide comments expressing sentiment about the entertainment value and/or expressing sentiment about how informative, intelligent or accurate the media content is. The sentiment provided in the comments (e.g., how funny the media content is) may be viewed by other users of the media hosting website in order to determine whether the media content appeals to them. However, to assess the overall sentiment or opinion regarding particular media content a user must read all of the comments about it. Due to the large volume of comments posted by viewers, reading all comments associated with the media content may be time-consuming and make it difficult for a user to assess overall sentiment expressed therein. Accordingly, there is a need for a way to identify user sentiment expressed within comments associated with media content. 
     SUMMARY 
     Embodiments of the present invention enable the generation and use of sentiment scores associated with media content, wherein the sentiment scores indicate different types of sentiment expressed in comments associated with the items of media content. The media content may be video, audio, text, still images or other types of media content. 
     An embodiment of a method according to the present invention comprises a computer-implemented method for generating a sentiment profile for an item of media content. A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content. 
     Another embodiment of a method according to the present invention comprises a computer-implemented method. A search query searching for media content is received, the search query indicating a type of sentiment associated with the searched-for media content. A set of search results is received responsive at least in part to the search query, the search results identifying a set of items of media content having the type of sentiment indicated by the search query, items in the set of media content having associated sentiment scores indicating types of sentiment expressed in comments associated with the items of media content. The set of search results is provided. 
     Another embodiment of a method according to the present invention comprises a computer-implemented method. A selection of an item of media content is received. A plurality of items of media content based on a plurality of sentiment similarity scores is received, wherein each sentiment similarity score indicates a similarity between types of sentiment expressed in comments associated with the selected item of media content. The plurality of items of media content is provided. 
     Another embodiment of a method according to the present invention comprises a computer-implemented method for displaying an item of media content on a display device. A set of sub-portions of an item of media content is identified, wherein each sub-portion is associated with a sentiment score indicating a magnitude of a type of sentiment expressed in a comment associated with the sub-portion of the item of media content. The item of media content is displayed on a display device, wherein the set of sub-portions of the item of media content are displayed in association with the magnitudes of the type of sentiment indicated by the sentiment scores associated with the sub-portions of the item of media content. 
     The features and advantages described in this summary and the following detailed description are not all-inclusive. Many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims hereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level block diagram of a computing environment according to one embodiment. 
         FIG. 2  is a screen shot illustrating a graphical user interface for providing a comment associated with an item of media content according to one embodiment. 
         FIG. 3  is a high level block diagram of a content comment server according to one embodiment. 
         FIG. 4  is a flow chart illustrating steps performed by a content comment server to process and store comments associated with media content according to one embodiment. 
         FIG. 5  is a flow chart illustrating steps performed by a content comment server to generate and store sentiment profiles associated with an item of media content according to one embodiment. 
         FIG. 6  is a flow chart illustrating steps performed by a content comment server to iteratively generate a sentiment classifier according to one embodiment. 
         FIG. 7  is a flow chart illustrating steps performed by a content comment server to calculate sentiment similarity scores according to one embodiment. 
         FIG. 8  illustrates a graphical user interface for searching media content according to types of sentiment expressed in comments associated with the media content according to one embodiment. 
         FIG. 9  illustrates a graphical user interface for displaying media content in association with sentiment profile information and related items of media content according to one embodiment. 
         FIG. 10  is a flow chart illustrating steps performed by the content comment engine to display media content in association with individual sentiment scores associated with sub-portions of the media content according to one embodiment. 
         FIG. 11  illustrates a graphical user interface for displaying media content in association with individual sentiment scores associated with sub-portions of the media content according to one embodiment. 
         FIG. 12  is a flow chart illustrating steps performed by a content comment engine to generate content sentiment scores for items of media content according to one embodiment. 
         FIG. 13  is a high-level block diagram illustrating a typical computer for use as a content comment server, a content provider or a content viewer. 
     
    
    
     The figures depict preferred embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein. 
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a content comment server  104 , a plurality of content viewers  106  and plurality of content providers  102  connected by a network  114 . Only two content providers  102  and three content viewers  106  are shown in  FIG. 1  in order to simplify and clarify the description. Embodiments of the computing environment  100  can have thousands or millions of content providers  102  and content viewers  106  connected to the network  114 . 
     The content comment server  104  communicates with content viewers  106  and content providers  102  over the network  114 . The content comment server  104  receives uploaded media content from content providers  102  and allows content to be viewed by content viewers  106 . Media content may be uploaded to the content comment server  104  via the Internet from a personal computer, through a cellular network from a telephone or PDA, or by other means for transferring data over the network  114 . Media content may be downloaded from the content comment server  104  in a similar manner; in one embodiment media content is provided as a file download to a content viewer  106 ; in an alternative embodiment, media content is streamed to the content viewer. The means by which media content is received by the content comment server  104  need not match the means by which it is delivered to a content viewer  106 . For example, a user of the content provider  102  may upload a video via a browser when the content provider is a personal computer, whereas a user of the content viewer  106  may view that video as a stream sent to a PDA, when the content viewer is a PDA. Note also that the content comment server  104  may itself serve as the content provider  102 . 
     A content provider  102  provides media content to the content comment server  104 . Examples of media content include audio, video, image and text content; other forms of content available for consumption may also be provided. The media content may have been created by a content provider  102 , but need not have been. 
     Content viewers  106  view media content provided by the content comment server  104  via a user interface. Typically, a content viewer  106  runs a web browser such as Microsoft Internet Explorer or Mozilla Firefox, and the content comment server  104  includes a web server such as Microsoft Internet Information Services. Using her browser, a user of content viewer  106  browses and searches for content provided by the content comment server  104  and views content of interest, including video content. In some embodiments, the content viewer uses other types of software applications to view, browse and search media content from the content comment server  104 . As described further below, the content viewer  106  also allows a user to provide comments to content comment server  104  in response to viewed media content. 
     The content comment server  104  further functions to generate sentiment profiles based on the received comments. Sentiment profiles are sets of scores indicating different types of sentiment expressed in comments associated with an item of media content such as a video, blog entry, new story or song. A type of sentiment, as used herein, refers to any aspect of the item of the media content which is subjectively evaluated. For example, the entertainment value of the media content is an aspect of the media content that is subjectively evaluated by viewers of media content and is inherently subject to differences in opinion. Typically, the different types of sentiment may be directed to aspects of the entertainment value of the media content such as how humorous, interesting, heartwarming or frightening the media content is. Depending on the embodiment, other types of sentiment may be directed to the information value of the media content such as: the accuracy of the media content, the intellectual value of the media content, or how controversial the media content is. Other types of sentiment are well known. The content viewer  106  may use these profiles to browse or search media content on the content comment server  104 . 
       FIG. 2  illustrates a user interface  200  for displaying media content on the content viewer  106  according to one embodiment. In the embodiment illustrated, the user interface  200  displays video content, comments, and information about the video content. In alternate embodiments, the user interface  200  may display other types of media content such as image content, textual content or audio content. In alternate embodiments, the user interface  200  may display other types of media content such as image content, textual content or audio content. 
     The user interface  200  displays an item of media content  205  and comments  221 ,  222 ,  223 ,  224  submitted by content viewers  106  expressing sentiment about the item of media content. The user interface  200  further displays the number of comments  210  associated with the item of media content submitted by content viewers  106 . The user interface  200  further displays information associated with the media content  205  that can be used to uniquely identify the media content such as images associated with media content or the author of the media content. 
     The user interface  200  displays a comment posting user-interface object  220  that the content viewer  106  may select to post comments about the item of media content  205 . In some embodiments, the comment posting user-interface object  220  allows the content viewer to post comments in association with a specific temporal and/or spatial sub-portion of the media content. The user interface  200  also displays comment thread user-interface objects  230  which allow the content viewer  106  to create threads of comments by responding to comments submitted by other users. A thread of comments, as used herein, refers to a sequential series of comments submitted in response to other comments. The first comment in the thread of comments is herein referred to as the parent comment. The content viewer  106  further displays agree/disagree user interface objects  240  which allow users to agree or disagree with the comments submitted by other users. The content viewer  106  also displays a value  250  representing the number of user agreements or disagreements associated with the user submitted comment. 
       FIG. 3  is a high-level block diagram illustrating a detailed view of the content comment server  104  according to one embodiment. As shown in  FIG. 3 , the content comment server  104  includes several engines and servers. Those of skill in the art will recognize that other embodiments can have different engines and/or servers than the ones described here, and that the functionalities can be distributed among the engines and/or servers in a different manner. In addition, the functions ascribed to the content comment server  104  can be performed by multiple servers. 
     In alternate embodiments of the comment content server  104 , the media content database  330  and/or the media content server  310  may be hosted at one or more separate servers by different entities with the content comment server  104  acting as a third party server to process comments received by the media upload server  306  and generate sentiment profiles for the processed comments. 
     The media upload server  306  receives media content uploaded by the content providers  102 . The media content server  306  stores uploaded media content in the media content database  330 . The media upload server  306  further receives uploaded comments about the media content and other information such as ratings associated with the media content from the content viewers  106 . The media upload server  306  stores the uploaded comments in the comment database  350 . 
     The media content database  350  stores received media content in association with unique identifiers for that media content. According to the embodiment, the media content database  350  may store additional information about the media content such as an author of the media content, the date the media content was received by the content comment sever  104 , the subject of the media content, categories associated with the media content, tags associated with the media content and comments provided by an author of the media content. In some embodiments, the media content database  350  stores information derived from providing the media content to content viewers  106  such as ratings of the media content provided by the content viewers  106  and the frequency at which the media content is viewed by the content viewers  106 . 
     The media content server  310  provides information and media content to the content viewers  106 . The media content server  310  retrieves media content from the media content database  330 . The media content server  310  provides the retrieved media content to the content viewers  106 . The media content server  310  also retrieves comments from the comment database  350  and provides these comments to the content viewers  106 . The media content server  310  further functions to retrieve and provide information and media content responsive to search queries received from the content viewers  106 . The search queries may include criteria including the type of sentiment, search terms, etc. The media content server  310  retrieves items of media content associated with sentiment profiles indicating types of sentiment identified by the content viewer  106  and provides these items of media content to the content viewer  106 . The media content server  310  further retrieves items of media content related to a selected item of media based on sentiment similarity scores indicating a high similarity between sentiment profiles for the retrieved and selected items of media content and provides the retrieved items of media content to the content viewers  106 . The media content server  310  further retrieves individual sentiment scores associated with sub-portions of items of media content and provides the individual sentiment scores to the content viewers  106  for display association with the items of media content. 
     The comment engine  308  processes comments for storage in the comment database  350  and the sentiment comment corpus  380 . The comment engine  308  communicates with the media upload server  306  to receive uploaded comments associated with media content. The comment engine  308  also identifies comments in the sentiment comment corpus  380  that require processing. The comment engine  308  processes comments using a defined set of algorithms and transforms. 
     If the comment engine  308  identifies comments provided in non-textual media, such as audio, video or image content, the comment engine  308  generates textual comments from the received comments using techniques such as speech recognition and optical character recognition (OCR). 
     The comment engine  308  further generates natural language information about the textual comments by processing the textual comments using natural language processing (NLP) techniques. The comment engine  308  stores the natural language information in association with the comment from which it was generated and the item of media content in the comment database  250 . According to the embodiment, natural language information generated by the comment engine  308  may include tokens, part-of-speech tagged tokens, stemmed tokens, n-grams and phrases (e.g., noun phrases, verb phrases). According to the embodiment, natural language processing techniques and algorithms used by the comment engine  308  may include: parsing or tokenizing, punctuation extraction, stemming, negation detection, part-of-speech tagging, and use of regular expressions to identify pre-defined data. 
     In a one embodiment, the comment engine  308  parses the textual comments into sets of tokens and generates part-of-speech (POS) tags for the tokens using a probabilistic tagger and the following notation:
         Q is used to denote tokens representing Punctuation or Phrase-Breaking Markers.   P is used to denote tokens representing Pronouns other than “you”.   Y is used to denote tokens representing the Pronoun “you”.   M is used to denote tokens representing Modal Verbs (e.g., ‘can’).   A is used to denote tokens representing Adjectives.   R is used to denote tokens representing Adverbs.   N is used to denote tokens representing Nouns.   V is used to denote tokens representing Verbs.   O is used to denote tokens representing parts-of-speech that are other than the above listed parts-of-speech or unknown.       

     In one embodiment, the comment engine  308  selects sentiment-laden phrases based on the tagged tokens and stores the sentiment-laden phrases in the comment database  350 . According to the embodiment, sentiment-laden phrases may be selected using regular expressions or parsing techniques such as syntax trees or semantic grammars. In some embodiments, sentiment-laden phrases may be identified using a pre-defined lexicon of sentiment-laden phrases. In a specific embodiment, the comment engine  308  uses the following regular expressions to select sentiment-laden phrases:
         1. Adjective+Noun: “(.*?)(A+N+)( )” (e.g., funny video)   2. Adverb+Adjective+Noun: “(.*?)(R+A+N+)( )” (e.g., really funny video)   3. Model Verb+Verb+Adjective+Noun: “(.*?)(MV?A+N+)( )” (e.g., can make a funny video)   4. Pronoun+Verb+Adverb (optional)+Adjective+Noun: “(.*?)(PV?R*A+N+)( )” (e.g., I love this really funny video)   5. Punctuation+Verb+Adverb (optional)+Adjective+Noun, if preceded by punctuation: “Cl*?Q)(V+?R*A+N+)( )” (e.g., Love the funny video)   6. Noun/Pronoun+Verb+Adverb (optional)+Adjective: “(.*?)((?:N+IP)+V+R*A+) (QI $)” (e.g., this video is really funny)       

     In another embodiment, the comment engine  308  parses the comments into sets of tokens and combines the tokens into n-grams. N-grams are sets of tokens that are consecutive in the comments. In one embodiment, the comment engine  308  uses a sliding window technique to generate the n-grams. Using a sliding window technique, a window of n tokens (e.g., 3, 4, 5, or 6 tokens) is advanced by one token over the set of tokens representing the comment in order to produce a set of all n-grams in the comment. The comment engine  308  stores the n-grams in the comment database  330  in association with the comments. 
     The comment engine  308  further identifies colloquial sentiment information in the comments. Colloquial sentiment information is information that contains sentiment but is different in structure than written language. Therefore, most natural language processing algorithms cannot be applied to colloquial sentiment information. The comment engine  308  can identify colloquial sentiment information specified in a pre-defined library of colloquial sentiment information using simple searches of the comments or by applying regular expressions to the comments. Colloquial sentiment information includes but is not limited to: acronyms, slang and emoticons. In most embodiments, colloquial sentiment information identified in comments is specific to the Internet such as acronyms, slang or emoticons commonly used in communications on Internet websites (e.g., OMG, ©, LOL). Colloquial sentiment information is stored in association with the comments in which it is identified. 
     The comment database  330  stores comments received from the content viewers  106  regarding the media content. The comment database  330  stores the comments in association with unique identifiers for the media content. The comment databases  330  further stores information generated by the comment engine  308  such as textual comments derived from non-textual comments such as video or audio comments in association with the unique identifier for the comment. The comment database  330  further stores natural language information generated by the comment engine  308 . The comment database  330  further stores colloquial sentiment information identified by the comment engine  308  in association with unique identifiers for the comments. 
     According to the embodiment, the comment database  350  also may store annotation information which indicates a sub-portion of the media content that the comment is associated with. In one embodiment, annotation information indicates that a comment is associated with a temporal sub-portion (i.e., subset of time points) of an item of time-series media such a video or a song (e.g., a comment associated with a chorus of a song). In another embodiment, annotation information indicates a spatial sub-portion of media such as a video or image that the comment is directed to (e.g., a comment associated with a person in an image). In another embodiment, annotation information may indicate that the comment is associated with a sub-portion of text in textual media such as a book, website or a blog (e.g., a paragraph in a news story). 
     In some embodiments, the comment database  330  stores information regarding threads of comments. In these embodiments, the comment database  330  stores information indicating a parent comment and the sequential order of comments in the thread. In some embodiments, each thread may further be associated with sub-threads. The comment database  330  further stores information indicating the number of comments in each thread and the number of threads associated with an item of media content. 
     The sentiment comment corpus  380  stores comments about media content in association with labels indicating the types of sentiment expressed in the comments (e.g., sad, happy, inspiring. etc.). In some embodiments, the labels may further indicate the strength or magnitude of the types of sentiment expressed in the comments (e.g., very funny). In a specific embodiment, strength or magnitude may be represented as a continuous score (e.g., a level of funny from I to 5). According to the embodiment, the labels may be generated by an administrator or computationally through classification of comments stored in the comment database  350 . 
     The sentiment comment corpus  380  further stores natural language information and colloquial sentiment information generated by the comment engine  308 , as described above. According to the embodiment, the sentiment comment corpus  380  may associate labels indicating the type and magnitude of sentiment expressed in the comments with comments in their entirety or associate labels indicating the type and magnitude of sentiment expressed in the comments with information derived from the comments such as natural language information such as sentiment-laden phrases or colloquial sentiment information. In other embodiments, comments may be processed by the comment engine  308  before they are labeled with types of sentiment and magnitude values and stored in the sentiment comment corpus  380 . 
     In one embodiment, the sentiment comment corpus  380  associates labels indicating type and magnitude of sentiment with comments and/or natural language information based on a lexical database, such as the WordNet electronic lexical database available from Princeton University of Princeton, N.J. The lexical database describes mappings between related words. That is, the database describes synonym, antonym, and other types of relationships among the words. In one embodiment, an administrator of the content comment server  104  selects initial labels for comments and/or natural language information in the sentiment comment corpus  280  by reviewing the lexical database and manually selecting and scoring words expressing different types of sentiment. For example, a comment containing a token for the word “hilarious” could be labeled with the sentiment type “humorous” at a magnitude of 5 on a scale of 5. This initial set of words is expanded through an automated process to include synonyms (e.g., “funny”, “droll”) and antonyms referenced in the lexical database. The expanded set of words is then used either automatically or manually to assign labels and scores to comments and natural language information associated with the comments in the sentiment comment corpus  280 . In an alternate embodiment, the lexical database may be generated automatically or semi-automatically by training or re-training classifiers on the sentiment comment corpus  280 . In a specific embodiment, the sentiment comment corpus  280  is updated to include comments labeled by the sentiment profile engine  312  and the lexical database is re-generated based in part on the updated sentiment comment corpus  280 . 
     The sentiment comment corpus  380  further stores a library of colloquial sentiment information in association with labels indicating the type of sentiment that it is intended to express. For instance, the emoticon “©” may be associated with the sentiment type “happy” with a magnitude of “5”. Likewise, the acronym “LOL” may be associated with the sentiment type “humorous” with a magnitude of “4”. This library of colloquial sentiment information is used to automatically determine labels indicating the type of sentiment expressed in the comments for colloquial sentiment information stored in the sentiment comment corpus  380 . 
     The sentiment comment corpus  380  may further store information indicating the type of media represented in the media content to which the comment is directed (e.g., news story, video, movie, blog, song, etc.). 
     The sentiment profile engine  312  generates sentiment profiles for the items of media content based on the comments associated with the items of media content. The sentiment profile engine  312  generates individual sentiment scores for the comments by classifying natural language information and colloquial sentiment information associated with each of the stored comments using one or more sentiment classifiers  320 . Each individual sentiment score represents the likelihood that a type of sentiment is expressed in a comment associated with an item of media content. The sentiment profile engine  312  then aggregates individual sentiment scores for all comments associated with an item of media content indicating a type of sentiment and stores the aggregate sentiment scores in sentiment profiles associated with the item of media content. The sentiment profiles contain a set of aggregate scores which represent different types of sentiment expressed in comments associated with media content. Different types of sentiment expressed in the comments include but are not limited to: how humorous, heartwarming, cheerful, inspirational, informative, accurate, strange, sad, shocking, frightening, entertaining or intelligent the media content is. 
     The sentiment profile engine  312  generates the sentiment classifiers  320  based on the sentiment comment corpus  380 . The sentiment profile engine  312  identifies labeled natural language information and colloquial sentiment information in the sentiment comment corpus  380 . The sentiment profile engine  312  then generates a different sentiment classifier  320  for each type of sentiment by selecting any combination of the natural language information and colloquial sentiment information associated with the label indicating the type of sentiment. For example, the sentiment profile engine  312  may generate a sentiment classifier  320  for the sentiment type “frightening” based on any combination of the colloquial sentiment information and natural language information in the sentiment comment corpus  380  associated with a label indicating the sentiment type “frightening” (e.g., the sentiment laden phrase “very scary movie” and/or the emoticon “:O”). The sentiment classifier  320  is a statistical model which specifies a set of values, such as a set of co-efficient values, in association with the natural language information and/or colloquial sentiment information. The set of values specifies the correlation between natural language information and/or colloquial sentiment information and the label indicating the type of sentiment. 
     According to the embodiment, the sentiment profile engine  312  can generate the classifier for a sentiment type using any type of classification model  320 . Suitable classification models include but are not limited to: decision tree models, regression-based models, Bayesian models, support vector machines (SVMs) and neural network models. 
     In some embodiments, the sentiment profile engine  312  may generate a sentiment classifier  320  for a type of sentiment based on a set of comments, colloquial sentiment information or natural language information that are not associated with a label indicating the type of sentiment. The inclusion of the set of comments, colloquial sentiment information or natural language information that is not associated with a label indicating the type of sentiment provides a set of negative examples used to train a binary classifier to identify information associated with comments that do not indicate the type of sentiment. For example, the sentiment profile engine  312  may generate a sentiment classifier  320  for the sentiment type “frightening” based on any combination of the comments, colloquial sentiment information and natural language information in the sentiment comment corpus  380  that are associated with a label indicating the sentiment type “happy”. 
     The sentiment profile engine  312  applies the generated classifiers to comments in the comment database  330  to generate a set of individual sentiment scores for each comment. The individual sentiment scores represent the likelihood that the comment expresses particular types of sentiment. Typically, the individual sentiment scores are continuous scores. In some embodiments, the individual sentiment scores are subject to threshold values in order to assign binary scores or labels indicating whether or not the comment expresses the sentiment type. In these embodiments, the individual sentiment scores are represented as binary scores in association with the label. In some embodiments, the individual sentiment scores are discretized or normalized. Individual sentiment scores are stored in association with the comments in the comment database  330 . According to the embodiment, comments associated with individual sentiment scores strongly indicating that the comment expresses a sentiment type may be labeled with the sentiment type and stored in the sentiment comment corpus  380 . 
     The sentiment profile engine  312  aggregates the individual sentiment scores associated with comments expressing sentiment about an item of media content to generate aggregate sentiment scores representing the sentiment expressed in the comments. According to the embodiment, the sentiment profile engine  312  can aggregate the individual sentiment scores by averaging the individual sentiment scores, selecting a top percentile of individual sentiment scores or by using consensus methods. According to the embodiment, the aggregate sentiment scores may be binary scores, indicating whether or not types of sentiment are expressed in the majority of comments associated with an item of media content. In other embodiments, the aggregate sentiment scores may be continuous scores indicating likelihoods that types of sentiment are expressed in the comments associated with an item of media content or magnitude scores indicating strengths at which the types of sentiment are expressed in the comments associated with an item of media content. 
     In some embodiments, the sentiment profile engine  312  weights the individual sentiment scores before generating the aggregate sentiment scores. In one embodiment, the sentiment profile engine  312  weights the individual sentiment scores based on historic information associated with the user who provided the comment associated with the sentiment score. The historic information associated with the user may include ratings information or sentiment scores associated with other comments provided by the user. For instance, a high sentiment score for the sentiment type “cute” associated with a comment provided by a user who frequently submits comments with sentiment scores indicating “cute”, may be given a reduced weight. Conversely, a high sentiment score for a sentiment type “entertaining,” associated with a comment provided by a user who frequently gives media content poor ratings may be given an increased weight. 
     In one embodiment, the sentiment profile engine  312  weights the individual sentiment scores for a comment based on the number of agreements or disagreements submitted by content viewers  106  regarding the comment before aggregating the individual sentiment scores. For instance, a comment with 300 agreements would have a greater weight than a comment with 5 disagreements. In other embodiments, individual sentiment scores associated with comments that are parent comments in a thread of comments may be weighted by the number of other comments in the thread of comments. For instance, a comment with 300 replies may be deemed more interesting or controversial than a comment with no replies and be given a greater weight in determining the aggregate sentiment scores. Similarly, comments may be weighted by the order in which they appear in the thread. Comments which are closer to the parent comment may be given a greater weight than comments that occur later in the sequence specified by the thread. 
     In embodiments where comments are associated with sub-portions of items of media content, individual sentiment scores may be weighted by the proportion of the media the comments are associated with. For instance, an individual sentiment score associated with a 5 second long portion of a 10 minute long video would be given a lower weight in determining an aggregate sentiment score for the video than a comment associated with 5 minute long portion of the video. 
     The sentiment profile engine  312  stores the aggregate sentiment scores as sentiment profiles in association with the unique identifiers for the items of media content in the sentiment profile database  390 . The sentiment profile database  390  stores the set of aggregate sentiment scores in association with the type of sentiment the score represents as sentiment profiles. In instances where there is only a single comment associated with an item of media content, the sentiment profile engine  312  stores the individual sentiment scores for the comment as the sentiment profile in association with the item of media content in the sentiment profile database  390 . 
     The sentiment profile database  390  stores a sentiment profile for each item of media content in association with a unique identifier for the item of media content. Each sentiment profile contains one or more aggregate sentiment scores and each aggregate sentiment score represents a different type of sentiment expressed in comments associated with the media content. According to the embodiment, the sentiment scores may be binary scores indicating whether a type of sentiment is expressed in comments associated with the media content or may be a continuous score indicating a degree or level of a type of sentiment expressed in the comments associated with the media content. For example, sentiment scores of I or 0 may respectively indicate whether or not the comments associated with a video express the sentiment that the video is funny. In embodiments where the aggregate sentiment scores are binary, the sentiment profile store a series of binary scores in association with types of sentiment. For example, ({scary, I}, {boring, O}, {informative, O}, {cute, O}, {dramatic, I}, {heartwarming, O}, {wacky, I}, {controversial, I}). 
     According to the embodiment, continuous aggregate sentiment scores may either indicate the likelihood that a type of sentiment is expressed in comments or a strength at which a type of sentiment is expressed in comments. For example, a sentiment score of 90% for the sentiment type “informative” may indicate that the likelihood of an item of media content being “informative” based on the comments is 90%. Alternatively, a sentiment score of 5 for the sentiment type “happy” could indicate that the sentiment type happy has a strength of 5 based on the comments associated with the item of media content. In embodiments where the aggregate sentiment scores are continuous, the sentiment profile stores a series of continuous scores in association with types of sentiment. For example, ({scary, 8}, {boring, −2}, {informative, O}, {cute, O}, {dramatic, 6}, {heartwarming, −8}, {wacky, 5}, {controversial, 6}). 
     The sentiment similarity engine  314  generates sentiment similarity scores which indicate the correspondence between sentiment profiles associated with different items of media content. For each pair of sentiment profiles, the sentiment similarity engine  314  generates a sentiment similarity score based on the set of aggregate sentiment scores stored in the sentiment profiles. The sentiment similarity engine  314  generates a series of distance scores representing the correspondence between aggregate sentiment scores representing each sentiment type in the sentiment profiles. The sentiment similarity engine  314  combines the distance scores to generate a sentiment similarity score. According to the embodiment, the sentiment similarity engine  314  may generate and combine the distance scores to generate sentiment similarity scores using any suitable type of algorithm used to generate distance metrics such as Euclidean distance algorithms or Pearson correlation coefficient algorithms. The sentiment similarity engine  314  stores the sentiment similarity scores in association with the unique identifiers for the items of media content in the sentiment profile database  390 . In alternate embodiments, the sentiment similarity engine  314  does not generate pair-wise similarity scores but instead uses a greedy algorithm to identify a subset of the sentiment profiles with highest similarity scores with each sentiment profile. 
     The content classification engine  315  generates content classifiers  322  based on the sub-portions of media content associated with individual sentiment scores indicating the same type of sentiment. For instance, the content classification engine  315  may generate a content classifier  322  based on sub-portions of media content associated with individual sentiment scores indicating that the sub-portions of the media content are “boring”. According to the embodiment, the content classification engine  315  may select all sub-portions of media content associated with individual sentiment scores or only a set of sub-portions of media content with individual sentiment scores above a threshold value. For example, to generate a content classification  322  for the sentiment type “funny”, the content classifier engine  315  may select only sub-portions of media content with individual sentiment scores for “funny” that are greater or equal to 4 on a scale of 1-5. 
     The content classification engine  315  generates features from sub-portions of media content that can be used to classify media content. Features are data that can be used to characterize the media content. The generated features correspond to the media type of the media content. For still image content, features extracted may include: pixel intensity, luminosity, data derived from shape detection algorithms and other data derived from still images. For audio content, features extracted may include: pitch, tone, mel-frequency cepstral coefficients (MFC), and other data derived from audio content. For video content, features may include data derived from shot detection algorithms, face detection algorithms, edge detection algorithms, and other data derived from video content. 
     The content classification engine  315  generates a content classifier  322  for each type of sentiment based on the extracted features from the sub-portions of media content associated with individual sentiment scores indicating the sentiment type. According to the embodiment, the content classifier  322  may be any type of statistical model that specifies a correspondence between a feature from an item of media content and a type of sentiment. Suitable statistical models for use as content classifiers include but are not limited to support vector machines (SVMs), neural networks, Bayesian models and logistic regression models. 
     According to the embodiment, the content classifier engine  315  may apply the content classifier  322  to the received media content in the media content database  330  to generate content sentiment scores indicating the likelihood that an item of media content is associated with a type of sentiment. In some embodiments, the content sentiment scores for an item of media content may be stored in the sentiment profile database in association with the sentiment profiles for the item of media content. 
     According to the embodiment, the content sentiment scores may be used to cross-validate or supplement the aggregate sentiment scores stored in the sentiment profile. In one embodiment, a content sentiment score showing a high likelihood of a type of sentiment based on features extracted from the item of media content may be used to cross-validate an aggregate sentiment score based on comments associated with the item of media content and vice versa. For instance, a content sentiment score indicating a high likelihood that a video is dramatic may be used to validate an aggregate sentiment score indicating the same. In some embodiments, a content sentiment score showing a high likelihood of a type of sentiment based on features extracted from the item of media content may be further used to supplement or strengthen an aggregate sentiment score based on comments associated with the item of media content. For instance, a good agreement between a content sentiment score and an aggregate sentiment score for an item of media content could increase the content sentiment score for the item of media content. 
       FIG. 4  is a flowchart illustrating steps performed by the content comment server  104  to generate sentiment profiles in accordance with an embodiment of the present invention. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     The content comment server  104  receives  402  the media content from the content providers  102 . The content comment server  104  stores  404  the media content. The content comment server  104  provides  406  the media content to the content viewers  106 . The content comment server  104  receives  408  comments from users of the content viewers  106 . If the received comments are not textual comments, the content comment server  104  generates  410  textual comments based on the received comments. The content comment server  104  generates  412  natural language information based on the received comments. The content comment server  104  identifies  414  colloquial sentiment information based on the received comments. The content comment server  104  stores  416  the comments in association with the textual comments, natural language information and colloquial sentiment information in the comment database  330 . 
       FIG. 5  is a flowchart illustrating steps performed by the content comment server  104  to generate sentiment profiles for an item of media content in accordance with an embodiment of the present invention. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     The content comment server  104  identifies  512  comments associated with an item of media content in the comment database  350 . The content comment server  104  applies one or more sentiment classifiers  320  to the natural language information and colloquial sentiment information associated with each comment to generate  514  individual sentiment scores, each individual sentiment score indicating whether the comment expresses a type of sentiment. In some embodiments, the content comment server  104  stores  515  comments with an individual sentiment score indicating a high likelihood that the comment expresses a type of sentiment in association with a label for the type of sentiment in the sentiment comment corpus  380 . The content comment server  104  aggregates the individual sentiment scores indicating the same type of sentiment to generate  516  a set of aggregate sentiment scores. The content comment server  104  stores the set of aggregate sentiment scores representing the different sentiment types as a sentiment profile in the sentiment profile database. 
       FIG. 6  is a flowchart illustrating steps performed by the content comment server  104  to iteratively generate sentiment classifiers  320  in accordance with an embodiment of the present invention. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     The content comment server  104  identifies  602  all comments labeled with a particular type of sentiment in the sentiment comment corpus  380 . The content comment server  104  generates  604  a sentiment classifier  320  based on the colloquial sentiment information and the natural language information associated with the labeled comments. Upon identifying  606  updates to the sentiment comment corpus, the content comment server  104  iteratively identifies the newly labeled comments and re-generates the sentiment classifier  320 . 
       FIG. 7  is a flowchart illustrating steps performed by the content comment server  104  to calculate sentiment similarity scores between sentiment profiles. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     The content comment server  104  identifies sentiment profiles associated with items of media content. The content comment server  104  calculates sentiment similarity scores between each pair of items of media content based on their associated sentiment profiles. The content comment server  104  stores the sentiment similarity scores in the sentiment profile database  390 . 
       FIG. 8  is a screenshot illustrating a user interface  800  for searching media content on the content viewer  106 . In the user interface  800  illustrated, the media content is video content. In other embodiments, the media content may be audio content, textual content or image content. 
     The illustrated user interface  800  provides several search fields the content viewer uses to query the media content stored on the content comment server  104 . These fields include fields  860  which allow the user to provide search terms used to search textual information associated with the media content such as titles of the media content or summaries of the media content. Graphical user interface elements  820  are provided which allow the user to refine search results by selecting from several different sentiment types. In the embodiment illustrated, the user may select different sentiment types using check boxes  820 . The user submits a query including the search terms and/or selected sentiment types using a search button  830 . In other embodiments, the user may further provide other search criteria such as a selection to retrieve the most frequently viewed media content, the highest rated media content, or the media content most recently submitted to the media content database  350 . 
     Upon submission, the query is transmitted to the comment content server  104 . The media content server  310  receives the query. The media content server  310  identifies unique identifiers for a set of items of media content associated with aggregate sentiment scores indicating a high likelihood that the comments associated with the media content express the selected sentiment types from the sentiment profile database  390 . The media content server  310  then retrieves the set of items of media content indicated by the unique identifiers from the media content database  350  and provides the retrieved set of items of media content to the content viewer  104  as a set of search results. If search terms and/or other criteria are provided by the user, the media content server  310  filters the set of items according to search terms and/or other criteria provided by the user and provides a filtered set of items to the content viewer  104 . 
       FIG. 9  illustrates a user interface  900  for displaying an item of media content and items of related media content in association with sentiment profile information. In the user interface  900  illustrated, the media content is video content. In other embodiments, the media content may be audio content, textual content or image content. 
     An item of media content  915  is selected and displayed for viewing in the graphical user interface  900 . When selected for viewing, information indicating the types of sentiment expressed in comments associated with the item of media content  902  is displayed in association with the item of media content  915  in the graphical user interface  900 . 
     Upon selection of the item of media content for display, a set of related items of media content is retrieved by the content comment server  104  and provided for display. The content comment server  104  identifies a set of related items of media content based on sentiment similarity scores stored in the sentiment profile database  390  which indicate a high similarity between the sentiment profiles of the related items of media content and the selected item of media content. In one embodiment, the content comment server  104  identifies related items of media content with sentiment similarity scores that are higher than a defined threshold. In some embodiments, the content comment server  104  identifies a defined number of related items of media content with the highest sentiment similarity scores. 
     In a related-items pane  910 , the content comment server  104  displays the related items of media content  912 ,  914 , and  916  in a ranking based on the sentiment similarity scores. Items of media content with sentiment similarity scores indicating sentiment profiles similar to that of the selected item of media content  950  are displayed in association with the types of sentiment that strongly contribute to the similarity in the sentiment profiles between the selected and related items of media content represented by the sentiment similarity scores. 
       FIG. 10  is a flowchart illustrating steps performed by the content comment server  104  to display sentiment scores in accordance with an embodiment of the present invention. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     A set of comments associated with sub-portions of an item of media content is identified  1002 . Individual sentiment scores are identified  1004  for each of the comments associated with the sub-portions of the item of media content. The individual sentiment scores for the comments associated with sub-portions of the item of media content are displayed  1006  in association with the sub-portions of the item of media content on the content viewer. 
       FIG. 11  illustrates a user interface  1100  for displaying an item of media content and items of related media content according to sentiment profile information. In the user interface  1100  illustrated, the media content is video content. In other embodiments, the media content may be audio content, textual content or image content. 
     The user interface contains a display window  1110  for displaying an item of media content. In the illustrated interface, the display window  1110  displays an item of temporal media content (e.g., video content). The display window  1110  further displays controls  1130  including a temporal axis corresponding to the temporal media content. Within the display window, a score display object  1120  displays individual sentiment scores for a sentiment type in association with their corresponding sub-portions of the item of media content. In the score display control  1120  illustrated, a line plot corresponding to the temporal axis is used to display binary individual sentiment scores associated to temporal sub-portions of the video associated with comments. In the example illustrated, one temporal sub-portion of the video is associated with a comment expressing the sentiment type “boring” and other temporal sub-portions of the video are not associated with comments expressing the sentiment type “boring”. The temporal sub-portions of the video are indicated by their correspondence to the temporal axis representing the video. 
     In other embodiments, the score display object  1120  may plot individual sentiment scores indicating multiple sentiment types in a line plot over the temporal axis. In alternate embodiments, the score display object  1120  displays continuous individual sentiment scores in association with their corresponding temporal sub-portions of an item of temporal media content. 
     In alternate embodiments, the individual sentiment scores associated with sub-portions of an item of media content may be displayed in association with spatial data. In some embodiments, sub-portions of spatial media content such as text or images may be colored or labeled to indicate individual sentiment scores. For example, textual media content such as a news story may be colored on a gradient (e.g., from light pink to dark red) according to the magnitude of individual sentiment scores of the same type associated with sub-portions of the textual media. In this example, a paragraph or sentence associated with an individual sentiment score indicating a high degree of controversy may be colored a deeper shade of red then a paragraph or sentence with an individual sentiment score indicating that it is indicated to be mildly controversial. 
       FIG. 12  is a flowchart illustrating steps performed by the content comment server  104  to generate content sentiment scores in accordance with an embodiment of the present invention. Other embodiments perform the illustrated steps in different orders, and/or perform different or additional steps. Moreover, some of the steps can be performed by engines or modules other than the content comment server  104 . 
     The content comment server  104  identifies  1210  sub-portions of media content associated with individual sentiment scores from the sentiment profile database  390  and sentiment comment corpus  380 . The content comment server  104  generates  1212  feature sets for each of the sub-portions of media content. The content comment server  104  generates  1216  content classifiers  332  for sentiment types based on the features sets for sub-portions of media content associated with individual sentiment scores indicating the sentiment types. The content comment server  104  generates  1218  feature sets for unlabeled items of media content. The content comment server  104  generates content sentiment scores  1220  for items of media content based on applying the content classifiers  332  to features generated for the unlabeled items of media content. 
       FIG. 13  is a high-level block diagram illustrating a typical computer  1300  for use as a content comment server  104 , a content provider  102  or a content viewer  106 . Illustrated are a processor  1302  coupled to a bus  1304 . Also coupled to the bus  1304  are a memory  1306 , a storage device  1308 , a keyboard  1310 , a graphics adapter  1312 , a pointing device  1314 , and a network adapter  1316 . A display  1318  is coupled to the graphics adapter  1312 . 
     The processor  1302  may be any general-purpose processor such as an INTEL x86 compatible-CPU. The storage device  1308  is, in one embodiment, a hard disk drive but can also be any other computer-readable storage medium capable of storing data, such as a writeable compact disk (CD) or DVD, or a solid-state memory device. The memory  1306  may be any type of computer-readable storage medium, for example, firmware, read-only memory (ROM), non-volatile random access memory (NVRAM), and/or RAM, and holds instructions and data used by the processor  1302 . The pointing device  1314  may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard  1310  to input data into the computer  1300 . The graphics adapter  1312  displays images and other information on the display  1318 . The network adapter  1316  couples the computer  1300  to the network  114 . 
     As is known in the art, the computer  1300  is adapted to execute computer program modules. As used herein, the term “module” refers to computer program logic and/or data for providing the specified functionality. A module can be implemented in hardware, firmware, and/or software. In one embodiment, the modules are stored on the storage device  1308 , loaded into the memory  1306 , and executed by the processor  1302 . 
     The types of computers  1300  utilized by the entities of  FIG. 1  can vary depending upon the embodiment and the processing power utilized by the entity. For example, a content provider  102  that is a mobile telephone typically has limited processing power, a small display  1318 , and might lack a pointing device  1314 . The content comment server  104 , in contrast, may comprise multiple blade servers working together to provide the functionality described herein. 
     The present invention has been described in particular detail with respect to a limited number of embodiments. Those of skill in the art will appreciate that the invention may additionally be practiced in other embodiments. First, the particular naming of the components, capitalization of terms, the attributes, data structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms that implement the invention or its features may have different names, formats, or protocols. Further, the system may be implemented via a combination of hardware and software, as described, or entirely in hardware elements. Also, the particular division of functionality between the various system components described herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead performed by a single component. For example, the particular functions of the content comment server  104  may be provided in many or one module. 
     Some portions of the above description present the feature of the present invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are the means used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art. These operations, while described functionally or logically, are understood to be implemented by computer programs. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules or code devices, without loss of generality. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the present discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by real time network operating systems. 
     The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description above. In addition, the present invention is not described with reference to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references to specific languages are provided for disclosure of enablement and best mode of the present invention. 
     Finally, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention.