Using Combined Lexical Resources to Identify Hashtag Types

This paper seeks to identify sentiment and non-sentiment bearing hashtags by combining existing lexical resources. By using a lexicon-based approach, we achieve 86.3% and 94.5% precision in identifying sentiment and non-sentiment hashtags, respectively. Moreover, results obtained from both of our classification models demonstrate that using combined lexical, emotion and word resources is more effective than using a single resource in identifying the two types of hashtags.