OPTIONS OF THE SENTIMENT ANALYSIS IN XLSTAT The score of each term present in the document is multiplied by its frequency, then scores are summed to compute the document score. XLSTAT suggests using the Feature extraction tool, before going on sentiment analysis to get the document-term matrix. Sentiment analysis with NRC dictionary (emotion scale): This dictionary labels 13901 English terms with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive).īesides a sentiment dictionary, sentiment analysis needs tokenized documents. A term is labeled as "negative" if its score is lower than 0, and on the contrary, a term is labeled as "positive" if its score is greater than 0. Sentiment analysis with AFINN dictionary: 3382 English terms are rated between -5 and 5 (integer only) in the AFINN dictionary. Sentiment analysis with Syuzhet dictionary: 10748 English terms are rated between -1 and 1 in the Syuzhet dictionary. A term labeled as "negative" get a score of -1, a term labeled as "neutral" get a score of 0, and on the contrary, a term labeled as "positive" get a score of 1. Sentiment analysis with Bing dictionary: 6789 English terms are labeled as "negative", "neutral" or "positive" in the Bing dictionary. Dictionaries use different scales which is why XLSTAT suggests four sentiment dictionaries to assign sentiment values to terms: Sentiment analysis uses a dictionary where terms are scored or categorized in a polarity way (positive, negative, or neutral). In general, sentiment analysis answers "How do people feel about something?". Sentiment analysis helps companies to understand customers' reviews or feedback, product review, or analyze comments on the web (as tweets, or posts), and political discussions. The document can be labeled as a positive, negative, or neutral opinion. Sentiment analysis allows you to label a comment, a book, or in general a document. Sentiment analysis is the process of extracting an author's emotional intent from the text (Ted Kwarler, 2017).
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