Affiliations: [a] Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea
| [b] Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland
| [c] Faculty of Engineering and Information Technology, Quang Binh University, Dong Hoi, Vietnam | [d] Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh city, Vietnam
Correspondence:
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Corresponding author. Dosam Hwang, Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea. E-mail: [email protected].
Abstract: Sentiment analysis has been gaining importance in many applications such as recommendation systems, the decision making support and prediction models. Sentiment analysis helps to understand and evaluate public opinion regarding social events, product services, and political trends, especially the feelings expressed through comments by users in social networks such as Twitter, Facebook, and Instagram. There have been a lot of research attempts to address the tweets sentiment analysis problem with high accuracy, particularly in case of tweets that express a single sentiment towards a single object. However, the results of the classification are not highly accurate in cases such as the following: a user expresses multiple sentiments towards a single object in a tweet; a user presents multiple sentiments towards multiple objects; and a user indicates a single sentiment towards multiple objects. Furthermore, the previous studies only analyze the sentiment of each tweet without considering the objects and the sentiment towards each object from an entire set of tweets. This study attempts to deal with the limitations of the previous methods; an approach is proposed herein, based on integrating the sentiment towards a particular object from all tweets related to that object. The proposed method focuses on determining the objects and indicating the sentiment towards the specific objects by combining the sentiments related to each object from the entire set of tweets. On experimental evaluation, the proposed method is observed to have achieved a fairly good result in terms of the error ratio and achieved information.
