# Large Scale Implementations for Twitter Sentiment Classification
## 2 Related Work  
### Sentiment Analysis and Classification Models  
In the last decade, there has been an increasing interest in studies of Sentiment Analysis as well as emotional models. This is mainly due to the recent growth of data available in the World Wide Web, especially of those that reflect people's opinions, experiences and feelings [12]. Early opinion mining studies focus on document level sentiment analysis concerning movie or product reviews [13; 14] and posts published on web pages or blogs [15]. Sentiment Analysis is studied in many different levels. In [16], authors implement an unsupervised learning algorithm that classifies reviews, thus performing document level classification. Due to the complexity of document level opinion mining, many efforts have been made towards the sentence level sentiment analysis. The solutions presented in [17; 18; 19] examine phrases and assign to each one of them a sentiment polarity (positive, negative, neutral). A less investigated area is the topic-based sentiment analysis [20; 21] due to the difficulty to provide an adequate definition of topic and how to incorporate the sentiment factor into the opinion mining task. The most common approaches to confront the problem of sentiment analysis include machine learning and/or natural language processing techniques. [PERSON] et al. [22] used Naive Bayes, Maximum Entropy and Support Vector Machines classifiers so as to analyze sentiment of movie reviews; they classify movie reviews as positive or negative, and perform a comparison between the methods in terms of classification performance. [PERSON] and [PERSON] [23] utilized classification models with the aim of mining the sentiment out of multilingual web texts. On the other hand, the authors in [24] investigate the proper identification of semantic relationships between the sentiment expressions and the subject within online articles. Together with a syntactic parser and a sentiment lexicon, their approach manages to augment the accuracy of sentiment analysis within web pages and online articles. Furthermore, the method described in [25] defines a set of linguistic rules together with a new opinion aggregation function to detect sentiment orientations in online product reviews. Nowadays, Twitter has received much attention for sentiment analysis, as it provides a source of massive user-generated content that captures a wide aspect of published opinions. In [26], tweets referring to Hollywood movies are analyzed. They focused on classifying the tweets and in following on analyzing the sentiment about Hollywood movies in different parts of the world. Other studies that investigate the role of emoticons on sentiment analysis of tweets are the ones in [27; 28]. In both works, Lexicons of Emoticons are used to enhance the quality of the results. Authors in [29] propose a system that uses an SVM (Support Vector Machine) classifier alongside a rule-based classifier so as to improve the accuracy of the system. In [30], the authors proceed with a two-step classification process. In the first step, they separate messages as subjective and objective, and, in the second step, they distinguish the subjective tweets as positive or negative. There is a lot of research interest in studying different types of information dissemination processes on large graphs and social networks. [PERSON] et al. [31] analyze tweet posts and forecasts for a given post and the likelihood of being retweeted on its content. Authors indicate that tweets containing negative emoticons are more likely to be retweeted than tweets with positive emoticons. [PERSON] et al. [6] investigate the use of a tree kernel model for detecting sentiment orientation in tweets. A three-step classifier is proposed in [8] that follows a target-dependent sentiment classification strategy. Moreover, a graph-based model is proposed in [2] to perform opinion mining in Twitter data from a topic-based perspective.