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Related APIs in Category: Tweets
Discover the emotions expressed by the audience into their tweets, comments or chats and build rules, priority lists and alerts based on the emotions expressed. Qemotion is editing the most complete api to detect not only sentiment (positive/negative) but also qualitative analytics like the real emotions expressed into texts. Among other analytics, we’re automatically detecting: Emotional e-index © Qemotion France SAS Main Primary emotions by sentence Primary emotions intensity and breakdown (Happiness, Surprise, Sadness, Fear, Anger, Disgust) Speech engagement © Qemotion France SAS including Personal commitment, Time orientation, etc. Sensations (Taste, Smell, Sight, Touch, Suffering, etc.) - *BETA Mode Other emotional KPI (Valence, arousal, Dominance) Thematics & Topics of the speech (= Emotional triggers) English and French are currently available and 30 other spoken idiomas will be available soon. * Please contact us if interested to get more details The public API is free but limited. test can be easily with mashape. Do not hesitate to send us an email to [email protected] if you want to get an access. We will reply you in minutes and it’s free for limited volumes and non business purposes.
RussianSentimentAnalyzer (RSA) is a JSON API based on the technology stack of Insider Solutions company. It is capable of parsing the input text, reconstructing the meaning of messages with typos, like tweets and finding sentiment polarity oriented towards a particular object. Consider an example: I like new GalaxyS, but do not enjoy new iPhone. If there are no objects, the sentiment of this sentence can be detected as NEUTRAL or MIXED. If, however, GalaxyS has been passed in as an object, the sentiment will be POSITIVE. It will be NEGATIVE for iPhone in this particular example. Currently the API supports Russian language with input texts varying from long formal news posts to informal and short tweets. Looking for text analytics APIs? Check the full list here: https://www.mashape.com/dmitrykey
Sentiment analysis for text enrichment. The API returns whether the sentiment is likely to be positive, negative or neutral, and provides a score indicating how likely the probability is. The text is processed using natural language techniques, splitting the text into its component parts and then compared to a sentiment database that's trained and calibrated to work best on social data, such as tweets, blog post comments or social media posts.