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You Are What You Tweet: A New Hybrid Model for Sentiment Analysis

  • Conference paper
  • First Online:
Machine Learning and Data Mining in Pattern Recognition (MLDM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10358))

Abstract

The rise of social media has provided new opportunities to study human emotions through self-reported information such as text, emojis/emoticons, and geo-locations. Research has shown that hybrid models which integrate lexicons and machine learning methods can improve the accuracy of sentiment prediction. We propose the Normalized Difference Sentiment Index (NDSI) to identify frequently-occurring words that are predictive of positive or negative sentiments. Furthermore, we propose e-senti, a new hybrid model which combines 3 attributes (lexicons, a new NDSI word rank list, and tweet features) into a random forest classifier. We contribute to the methodology of sentiment analysis by introducing a model that is easy to implement, efficient, and accurate. We compare four widely used lexicons and find the AFINN lexicon most effective and efficient for our model. We test the e-senti model based on the sentiment140 data and tweets from Los Angeles County, California. Our results show that the maximum accuracy for the sentiment140 data is 86.1% and for our Los Angeles County data is 74.6%, outperforming most existing methods. Our future work will link the geo-tagged sentiment data to land use data to reveal how emotions and the built environment are connected.

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Correspondence to Arthur Huang .

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Huang, A., Ebert, D., Rider, P. (2017). You Are What You Tweet: A New Hybrid Model for Sentiment Analysis. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. Lecture Notes in Computer Science(), vol 10358. Springer, Cham. https://doi.org/10.1007/978-3-319-62416-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-62416-7_29

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-62416-7

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