Stability of a Multilingual Sentiment Analysis based on Word-to-Word Translations
People’s sentiments are known to have a large impact on changes in stock prices, products sales, and trends. Since web users generally state their opinion in various languages, it is important to develop a method of multilingual sentiment analysis for web texts. In this study, we design a multilingual sentiment analysis method based on word-to-word translation. The method classifies sentences by using a sentiment dictionary in a native language. The method consists of three phases: morphological analysis of a sentence, sentiment extraction of each word with the senti-ment dictionary, and sentiment extraction of a sentence based on words sentiments. We conduct sentiment classification experiments for sentences in English, German, French, and Spanish. In the experiments, we compare our method with three previous methods by the evaluation metrics “Accuracy,” “Precision,” “Recall,” and “F1-score.” The experimental results show that our method has an advantage on the stability for variations of languages.
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