This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

/pdf/semeval-2017-task-4-sentiment-analysis-in-twitter-5294l6x1f7.pdf

SemEval-2017 Task 4: Sentiment Analysis in Twitter

This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.

SemEval-2016 Task 4: Sentiment Analysis in Twitter

Sentiment analysis in Twitter is a field that has recently attracted research interest. Twitter is one of the most popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. The presented studies are categorized according to the approach they follow. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.

Like It or Not: A Survey of Twitter Sentiment Analysis Methods

With the rise of social networking epoch, there has been a surge of user generated content. Microblogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time microblogging service, Twitter, where users post real time reactions to and opinions� about� “everything”. In this paper, we expound a hybrid approach using both corpus based and dictionary based methods to determine the semantic orientation of the opinion words in tweets. A case study is presented to illustrate the use and effectiveness of the proposed system.

https://ijcsi.org/papers/IJCSI-9-4-3-372-378.pdf

Sentiment Analysis on Twitter

Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author. This chapter summarizes the diverse landscape of tasks and applications associated with sentiment analysis. We outline key challenges stemming from the complexity and subtlety of language use, the prevalence of creative and non-standard language, and the lack of paralinguistic information, such as tone and stress markers. We describe automatic systems and datasets commonly used in sentiment analysis. We summarize several manual and automatic approaches to creating valence- and emotion-association lexicons. We also discuss preliminary approaches for sentiment composition (how smaller units of text combine to express sentiment) and approaches for detecting sentiment in figurative and metaphoric language—these are the areas where we expect to see significant work in the near future.

/pdf/sentiment-analysis-detecting-valence-emotions-and-other-424wj1trso.pdf

Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

The proliferation of Web-enabled devices, including desktops, laptops, tablets, and mobile phones, enables people to communicate, participate and collaborate with each other in various Web communities, viz., forums, social networks, blogs. Simultaneously, the enormous amount of heterogeneous data that is generated by the users of these communities, offers an unprecedented opportunity to create and employ theories & technologies that search and retrieve relevant data from the huge quantity of information available and mine for opinions thereafter. Consequently, Sentiment Analysis which automatically extracts and analyses the subjectivities and sentiments (or polarities) in written text has emerged as an active area of research. This paper previews and reviews the substantial research on the subject of sentiment analysis, expounding its basic terminology, tasks and granularity levels. It further gives an overview of the stateof – art depicting some previous attempts to study sentiment analysis. Its practical and potential applications are also discussed, followed by the issues and challenges that will keep the field dynamic and lively for years to come.

/pdf/sentiment-analysis-a-perspective-on-its-past-present-and-1ds3zixzvn.pdf

Teeja Mary Sebastian

Papers

Sentiment Analysis on Twitter

Sentiment Analysis: A Perspective on its Past, Present and Future