![]() ![]() Term scores: Activate this option to display a bar chart showing the term score.ĭocument scores: Activate this option to display a bar chart showing the document score. We suggest increasing the minimum frequency when the number of terms increases. Minimum frequency: Enter the minimum frequency a term should have to be displayed in the term frequencies bar chart.Term frequencies: Activate this option to display a bar chart showing the total term frequencies. Result interpretations: Activate this option to display, under the result tables short interpretation. Note: Only available for the NRC dictionary. Sort by score (descending): Activate this option to sort the document scores in descending order.Įmotion frequencies by document: Activate this option to display a table that indicates the frequency of each emotion in each document.Note: Only available for the NRC dictionary.ĭocument scores: Activate this option to display a table showing the score of each document (row) according to the sentiment dictionary chosen in the General tab. Overall emotion frequencies: Activate this option to display the total frequency of each emotion present in all documents. Terms with a neutral sentiment, which means their score is equal to zero or they are not associated with an emotion, are not displayed. Display sentiment terms only: Activate this option to display only the sentiment terms.Term frequencies and associated emotion: Activate this option to display a table showing the total frequency and the associated emotion of each term including in the term frequencies selection. Note: Not available for the NRC dictionary. Term frequencies and scores: Activate this option to display a table showing the total frequency and the score of each term included in the term frequency selection. For this field, missing values are read as "neutral" or zero. If the "Column labels" option is activated, you need to include a header in the selection. This option allows you to define the sentiment of a term independently of the dictionary previously selected. If you choose the Bing dictionary as the sentiment dictionary, you must enter "negative", "neutral" or "positive". Sentiment dictionary: Choose among four sentiment dictionaries.Ĭustom scores: Select in this field two columns including the term and its score. One column corresponds to the frequencies of one term in each document. ![]() ![]() Term frequencies: Select in this field the term frequency matrix. 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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