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- W4313444698 abstract "Over the time, many lexicons have been developed for natural language processing. These are used as a baseline to learn the emotion recognition from texts. While most of them are annotated with the polarity of words, i.e. positive, negative or neutral for emotions recognition and sentiment analysis. However, they cover a limited number of words and even fewer lexicons can predict the harder task of emotions. “DepecheMood++” and “NRC” are currently the most comprehensive publicly available word-emotion lexicons for emotions that provide more detailed information on varied emotional parameters such as, happy, sad, fear, and angry. In this paper, we have investigated the performance by comparing the above two lexicons over a benchmark of the International Survey on Emotion Antecedents and Reactions (ISEAR) data set. Performance of aforementioned lexicals in an emotion recognition task is evaluated using F1-Measure. Also, machine learning classification algorithms such as “Naive Baye’s”, “Logistic Regression”, “K-Nearest Neighbours”, “Support Vector Machine”, and “Gaussian Naive Bayes” classifiers were utilized to compare the performance of the both lexicals. There are some notable differences between experimental results in the classification task." @default.
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- W4313444698 date "2023-01-01" @default.
- W4313444698 modified "2023-09-23" @default.
- W4313444698 title "Comparative Analysis of Lexicon-Based Emotion Recognition of Text" @default.
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- W4313444698 doi "https://doi.org/10.1007/978-981-19-5868-7_49" @default.
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