A Tool for Brand Analysis with Twitter Data

Abstract: As one of the largest Social Media in providing public data every day, Twitter has attracted the attention of researcher to investigate, in order to mine public opinion, which is known as Sentiment Analysis. Consequently, many techniques and studies related to Sentiment Analysis over Twitter have been proposed in recent years. However, there is no study that discuss about sentence pattern of positive/negative sentence and neither subjective/objective sentence. In this paper we propose POS sequence as feature to investigate pattern or word combination of tweets in two domains of Sentiment Analysis: subjectivity and polarity. Specifically we utilize Information Gain to extract POS sequence in three forms: sequence of 2-tags, 3-tags, and 5-tags. The results reveal that there are some tendencies of sentence pattern which distinguish between positive, negative, subjective and objective tweets. Our approach also shows that feature of POS sequence can improve Sentiment Analysis accuracy.

Keywords: Dimensions, Reach, Engagement, ROI.