Authors:
Sergiu Limboi
and
Laura Dioşan
Affiliation:
Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
Keyword(s):
Twitter Sentiment Analysis, Hashtag-based Features, Lexicon Features, Data Representation.
Abstract:
Twitter Sentiment Analysis is demanding due to the freestyle way people express their opinions and feelings. Using only the preprocessed text from a dataset does not bring enough value to the process. Therefore, there is a need to define and mine different and complex features to detect hidden information from a tweet. The proposed Twitter-Lex Sentiment Analysis system combines lexicon features with Twitter-specific ones to improve the classification performance. Therefore, several features are considered for the Sentiment Analysis process: only textual input from a tweet, hash-tags, and some flavors that combine them with the feature defined based on the result produced by a lexicon. So, the Vader lexicon is used to determine the sentiment of a tweet. This output will be appended to the four perspectives we defined, considering the features offered by Twitter. The experimental results reveal that our system, which focuses on the role of features in a classification process, outperfo
rms the baseline approach (use of original tweets) and provides good value to new directions and improvements.
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