Improvement of sentiment analysis via re-evaluation of objective words in SenticNet for hotel reviews
| Autor: | Hsien-Ming Chou, Chihli Hung, Wan-Rong Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Linguistics and Language Computer science media_common.quotation_subject Decision tree 02 engineering and technology Library and Information Sciences Lexicon computer.software_genre Language and Linguistics Education C4.5 algorithm 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences media_common business.industry 05 social sciences Sentiment analysis Ambiguity Support vector machine 020201 artificial intelligence & image processing Artificial intelligence Computational linguistics business computer Natural language processing |
| Zdroj: | Language Resources and Evaluation. 55:585-595 |
| ISSN: | 1574-0218 1574-020X |
| DOI: | 10.1007/s10579-020-09512-6 |
| Popis: | In order to extract the correct sentiment polarity from word of mouth (WOM), a wide-scale and well-organized sentiment lexicon is generally beneficial. SenticNet is one such lexicon. However, it consists of a high proportion of objective words, which are generally considered to be of little use for sentiment classification due to their ambiguity and lack of sentiments. In the literature, there is a dearth of models that focus on this issue. An objective word appearing more frequently in positive sentences than in negative sentences implies a strong relationship in a positive sentiment orientation, and conversely, an objective word appearing more frequently in negative sentences implies a strong relationship in a negative sentiment orientation. Thus, the ratio of an objective word appearing in positive and negative sentences provides the sentiment orientation. Based on this concept, this paper re-assigns the sentiment values to the objective words in SenticNet and builds a revised SenticNet. Three classification techniques, the J48 decision tree, support vector machine, and multilayer perceptron neural network are used for classification. According to the experiments, the proposed models which extract sentiment values from the revised SenticNet, significantly outperform those models which extract sentiment values from the original non-revised SenticNet. |
| Databáze: | OpenAIRE |
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