Reviewerly/exHarmony huggingface dataset:
Assessing Public Opinions of Products Through Sentiment Analysis

In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.
The Concept of Big Data in Bureaucratic Service Using Sentiment Analysis

The implementation of bureaucratic reform in Indonesia is not optimal and faces various obstacles. At present, public services demand excellent service and meet public satisfaction. The obstacles are rigid bureaucracy, incompetent bureaucrats or apparatuses, not professional, and there are technological gaps. Rapid technological development, such as digital technology and big data, has not been responded to positively by most bureaucrats. Big Data has a great potential for improving bureaucratic and public services. With a qualitative method and a waterfall software development life cycle, this article provides the design of a bureaucracy sentiment analysis application which implements Big Data technology for analyzing the opinions about bureaucratic service in Indonesia. This is for the purpose that the bureaucratic services can be improved based on societal opinion. The results of the experiment using RapidMiner showed that sentiment analysis as a Big Data technique for bureaucratic service based on societal opinion can be used to evaluate performance better.
Sentiment Analysis of Arabic Documents

Today, it is usual that a consumer seeks for others' feelings about their purchasing experience on the web before a simple decision of buying a product or a service. Sentiment analysis intends to help people in taking profit from the available opinionated texts on the web for their decision making, and business is one of its challenging areas. Considerable work of sentiment analysis has been achieved in English and other Indo-European languages. Despite the important number of Arabic speakers and internet users, studies in Arabic sentiment analysis are still insufficient. The current chapter vocation is to give the main challenges of Arabic sentiment together with their recent proposed solutions in the literature. The chapter flowchart is presented in a novel manner that obtains the main challenges from presented literature works. Then it gives the proposed solutions for each challenge. The chapter reaches the finding that the future tendency will be toward rule-based techniques and deep learning, allowing for more dealings with Arabic language inherent characteristics.
Sentiment Analysis and Sarcasm Detection (Using Emoticons)

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material and helps a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count-based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. Twitter is an online person-to-person communication administration where overall clients distribute their suppositions on an assortment of themes, talk about current issues, grumble, and express positive or on the other hand negative notions for items they use in life. Hence, Twitter is a rich source of information for supposition mining and estimation investigation.
Tools of Opinion Mining

Today's generation express their views and opinions publicly. For any organization or for individuals, this feedback is very crucial to improve their products and services. This huge volume of reviews can be analyzed by opinion mining (also known as semantic analysis). It is an emerging field for researchers that aims to distinguish the emotions expressed within the reviews, classifying them into positive or negative opinions, and summarizing it into a form that is easily understood by users. The idea of opinion mining and sentiment analysis tool is to process a set of search results for a given item based on the quality and features. Research has been conducted to mine opinions in form of document, sentence, and feature level sentiment analysis. This chapter examines how opinion mining is moving to the sentimental reviews of Twitter data, comments used in Facebook on pictures, videos, or Facebook statuses. Thus, this chapter discusses an overview of opinion mining in detail with the techniques and tools.
Machine Learning in Sentiment Analysis Over Twitter

Social networks are the main resources to gather information about people's opinions and sentiments towards different topics as they spend hours daily on social media and share their opinions. Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis's (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective. These tasks were performed at different levels of analysis ranging from the document level to the sentence and phrase level. Another task is aspect extraction, which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years, a large number of methods, techniques, and enhancements have been proposed for the problem of SA in different tasks at different levels. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets) in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets, which makes the processing difficult. The tweet format is very small, which generates a whole new dimension of problems like use of slang, abbreviations, etc. The chapter elaborately discusses three supervised machine learning algorithms—naïve Bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precisions, as well as recall values; f-measure; number of tweets correctly classified; number of tweets incorrectly classified; and execution time.
Implementation of Recurrent Network for Emotion Recognition of Twitter Data

A new generation of emoticons, called emojis, is being largely used for both mobile and social media communications. Emojis are considered a graphic expression of emotions, and users have been widely used to express their emotions in social media. Emojis are graphic unicode symbols used to express perceptions, views, and ideas as a shorthand. Unlike the small number of well-known emoticons carrying clear emotional content, hundreds of emojis are being used in different social networks. The task of emoji emotion recognition is to predict the original emoji in a tweet. Recurrent neural network is used for building emoji emotion recognition system. Glove is a word-embedding method used for obtaining vector representation of words and are used for training the recurrent neural network. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity. Based on the word embedding in the Twitter dataset, recurrent neural network builds the model and finally predicts the emoji associated with the tweets with an accuracy of 83%.
Experimenting Language Identification for Sentiment Analysis of English Punjabi Code Mixed Social Media Text

People do not always use Unicode, rather, they mix multiple languages. The processing of codemixed data becomes challenging due to the linguistic complexities. The noisy text increases the complexities of language identification. The dataset used in this article contains Facebook and Twitter messages collected through Facebook graph API and twitter API. The annotated English Punjabi code mixed dataset has been trained using a pipeline Dictionary Vectorizer, N-gram approach with some features. Furthermore, classifiers used are Logistic Regression, Decision Tree Classifier and Gaussian Naïve Bayes are used to perform language identification at word level. The results show that Logistic Regression performs best with an accuracy of 86.63 with an F-1 measure of 0.88. The success of machine learning approaches depends on the quality of labeled corpora.
An Extensive Text Mining Study for the Turkish Language

In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.
Depressive Person Detection using Social Asian Elephants' (SAE) Algorithm over Twitter Posts

With the advent of the web and the explosion of data sources such as opinion sites, blogs and microblogs appeared the need to analyze millions of posts, tweets or opinions in order to find out what thinks the net surfers. The idea was to produce a new algorithm inspired by the social life of Asian elephants to detect a person in depressive situation through the analysis of twitter social network. The proposal algorithm gives better performance compared to data mining and bioinspired techniques such as naive Bayes, decision tree, heart lungs algorithm, social cockroach's algorithm.
Rule-Based Polarity Aggregation Using Rhetorical Structures for Aspect-Based Sentiment Analysis

The segments of a document that are relevant to a given aspect can be identified by using discourse relations of the rhetorical structure theory (RST). Different segments may contribute to the overall sentiment differently, and the sentiment of one segment may affect the contribution of another segment. This work exploits the RST structures of relevant segments to infer the sentiment of a given aspect. An input document is first parsed into an RST tree. For each aspect, relevant segments with their relations in the resulting tree are localized and transformed into a set of features. A set of classification rules is subsequently induced and evaluated on data. The proposed framework performs well in several experimental settings, with the accuracy values ranging from 74.0% to 77.1% being achieved. With proper strategies for removing conflicting rules and tuning the confidence threshold, f-measure values for the negative polarity class can be improved.
Impact of Deep Learning on Semantic Sentiment Analysis

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.
Delete, Delete, Hang-Up

Whereas most members of social media are enthusiastically exercising their legal right to express themselves freely, some seem unwilling or incapable of assessing the high risk of disclosing information about their most private thoughts, interests, opinions, work, and health status, particularly in times of psychological distress or personal tragedy. This chapter updates criminal activity associated with frequent use of social media. Some believe that the conceptual elasticity of the term “cyberbullying” has been used to push for a tougher crime agenda, while obscuring tragedy of the suicides in Canadian federal parliamentary debates.
Learning Algorithms of Sentiment Analysis

The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.
Ontology-Based Opinion Mining for Online Product Reviews

In this chapter, the authors work at the feature level opinion mining and make a user-centric selection of each feature. Then they preprocess the data using techniques like sentence splitting, stemming, and many more. Ontology plays an important role in annotating documents with metadata, improving the performance of information extraction and reasoning, and making data interoperable between different applications. In order to build ontology in the method, the authors use (product) domain ontology, ConceptNet, and word net databases. They discuss the current approaches being used for the same by an extensive literature survey. In addition, an approach used for ontology-based mining is proposed and exploited using a product as a case study. This is supported by implementation. The chapter concludes with results and discussion.
Sentiment Analysis as a Restricted NLP Problem

With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.
Sentiment Analysis of Twitter Data

Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.
Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews

A large number of reviews are expressed on academic institutes using the online review portals and other social media platforms. Such reviews are a good potential source for evaluating the Indian academic institutes. This chapter aimed to collect and analyze the sentiments of the online reviews of the academic institutes and ranked the institutes on the basis of their garnered online reviews. Lexical-based sentiment analysis of their online reviews is used to rank academic institutes. Then these rankings were compared with the NIRF PR Overall University Rankings List 2017. The outcome of this work can efficiently support the overall university rankings of the NIRF ranking list to enhance NIRF's public perception parameter (PRPUB). The results showed that Panjab University achieved the highest sentiment score, which was followed by BITS-Pilani. The results highlighted that there is a significant gap between NIRF's perception rankings and the perception of the public in general regarding an academic institute as expressed in online reviews.
An Opinion Mining Approach for Drug Reviews in Spanish

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.
Hybrid Recommender System Using Emotional Fingerprints Model

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.
When Emotions Rule Knowledge

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.
Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS)

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.
Sentiment Analysis of Game Review Using Machine Learning in a Hadoop Ecosystem

Internet, &amp; more unambiguously the creation of WWW in the early 1990s, helped people to build an interconnected global platform where information can be stored, shared, and consumed by anyone with an electronic device which has the ability to connect to the Web. This provides a way of putting together lots of information, ideas, and opinion. An interactive platform was born to post content, messages, and opinions under one roof, and the platform is known as social media. Social media has acquired massive popularity and importance that why today almost everyone can't stay away from it. Social media is not only a medium for people to express their thoughts, moreover, but it is also a very powerful tool which can be used by businesses to focus on new and existing customers and increase profit with the help of social media analytics. This paper starts with a discussion on social media with its significance &amp; pitfalls. Later on, this paper presents a brief introduction of sentiment analysis in social media and give an experimental work on sentiment analysis in a social game review.