Sentiment analysis has recently gained considerable attention, since the classification of the emotional content of a text (online reviews, blog messages etc.) may have a relevant impact on market research, political science and many other fields. In this paper, we focus on the importance of the text preprocessing phase, proposing a new technique we termed lexical pattern-based feature weighting (LPFW) that allows one to improve sentence-level sentiment analysis by increasing the relevance of the features contained in particular lexical patterns. This approach has been evaluated on two sentiment classification datasets. We show that a systematic optimisation of the preprocessing filters is important for obtaining good classification accuracy. Also, we show that LPFW is effective in different application domains and with different training set sizes.

Improving sentiment analysis using preprocessing techniques and lexical patterns / Cagnoni, S.; Ferrari, L.; Fornacciari, P.; Mordonini, M.; Sani, L.; Tomaiuolo, M.. - In: INTERNATIONAL JOURNAL OF DATA ANALYSIS TECHNIQUES AND STRATEGIES. - ISSN 1755-8050. - 13:3(2021), pp. 171-185. [10.1504/IJDATS.2021.118022]

Improving sentiment analysis using preprocessing techniques and lexical patterns

Cagnoni S.;Fornacciari P.;Mordonini M.;Sani L.;Tomaiuolo M.
2021

Abstract

Sentiment analysis has recently gained considerable attention, since the classification of the emotional content of a text (online reviews, blog messages etc.) may have a relevant impact on market research, political science and many other fields. In this paper, we focus on the importance of the text preprocessing phase, proposing a new technique we termed lexical pattern-based feature weighting (LPFW) that allows one to improve sentence-level sentiment analysis by increasing the relevance of the features contained in particular lexical patterns. This approach has been evaluated on two sentiment classification datasets. We show that a systematic optimisation of the preprocessing filters is important for obtaining good classification accuracy. Also, we show that LPFW is effective in different application domains and with different training set sizes.
Improving sentiment analysis using preprocessing techniques and lexical patterns / Cagnoni, S.; Ferrari, L.; Fornacciari, P.; Mordonini, M.; Sani, L.; Tomaiuolo, M.. - In: INTERNATIONAL JOURNAL OF DATA ANALYSIS TECHNIQUES AND STRATEGIES. - ISSN 1755-8050. - 13:3(2021), pp. 171-185. [10.1504/IJDATS.2021.118022]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2902789
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