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A:
Two types of techniques have been used in the literature for semantic orientation-based approach for sentiment analysis, viz., (i) corpus based and (ii) dictionary or lexicon or knowledge based.
In this chapter, we explore the corpus-based semantic orientation approach for sentiment analysis.
Corpus-based semantic orientation approach requires large dataset to detect the polarity of the terms and therefore the sentiment of the text.
The main problem with this approach is that it relies on the polarity of the terms that have appeared in the training corpus since polarity is computed for the terms that are in the corpus.
This approach has been explored well in the literature due to the simplicity of this approach [29, 120].
This approach initially mines sentiment-bearing terms from the unstructured text and further computes the polarity of the terms.
Most of the sentiment-bearing terms are multi-word features unlike bag-of-words, e.g., “good movie,” “nice cinematography,” “nice actors,” etc.
Performance of semantic orientation-based approach has been limited in the literature due to inadequate coverage of the multi-word features.
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C:
Agarwal, B., & Mittal, N. (2016). Semantic Orientation-Based Approach for Sentiment Analysis.
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K:
Sentiment Analysis, Multi-word Features, Semantic Orientation, Seed Word List, Mutual Information Method
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P:
https://link.springer.com/chapter/10.1007/978-3-319-25343-5_6
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S:
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R:
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G:
https://app.razzi.my/findgref?gid=1sICPEHZfxAmrC9Xse2vjyKQ7-vImSElF
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