In this work we present sentiment analysis of messages posted on a medical forum. We categorize posts, written in English, into five categories: encouragement, gratitude, confusion, facts, and facts + sentiments. Our study applies a manual sentiment annotation, affective lexicons in its sentiment analysis and machine learning classification of sentiments in these texts. We report empirical resu...

Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics’ feelings towards policies, brands, business, etc. In this paper we present SentiCircles, a platform that captures feedback from social media conversations and applies contextual and conceptual sentiment analysis models to extract and summarise sentiment from these convers...

Information sharing on the Web has also led to rise and spread of fake news. Considering that information is generally written trigger stronger feelings from readers than simple facts, sentiment analysis been widely used detect Nevertheless, sarcasm, irony, even jokes use similarwritten styles, making distinction between fact harder catch automatically. We propose a new news Classifier consider...

Sentiment analysis of Twitter data is performed. The researcher has made the following contributions via this paper: (1) an innovative method for deriving sentiment score dictionaries using an existing sentiment dictionary as seed words is explored, and (2) an analysis of clustered tweet sentiment scores based on tweet length is performed.

This paper focuses on the task of disambiguating polarity-ambiguous words and the task is reduced to sentiment classification of aspects, which we refer to sentiment expectation instead of semantic orientation widely used in previous researches. Polarity-ambiguous words refer to words like” large, small, high, low ”, which pose a challenging task on sentiment analysis. In order to disambiguate ...

The tracking of citizens’ reactions in social media during crises has attracted an increasing level of interest in the research community. In particular, sentiment analysis over social media posts can be regarded as a particularly useful tool, enabling civil protection and law enforcement agencies to more effectively respond during this type of situation. Prior work on sentiment analysis in soc...

This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment and participate in all subtasks, i.e. two-point, three-point, and five-point scale sentiment classification and two-point and five-point scale sentiment quantification. We achieve competitive results for two-point sca...

Sentiment trend of twitter users have a great influence on their friends and the crowd listened. This paper directs at the user sentiment state of twitter, the unique medium, and applies set pair analysis method for trend analysis. First, we begin with set pair contact degree, then based on set pair affective computing model to make comparison with the size relationship of same degree, differen...

Chinese microblog is a popular Internet social medium where users express their sentiments and opinions.But sentiment analysis onChinese microblogs is difficult: The lack of labeling on the sentiment polarities restricts many supervised algorithms; out-of-vocabulary words and emoticons enlarge the sentiment expressions, which are beyond traditional sentiment lexicons. In this paper, emoticons i...

Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 competition, in the sentiment analysis of Twitter and SMS messages. Our approach for this task is the combination of two sentiment analysis subsystems which are combined together to build the final system. Both subsystems use supervised le...