Twitter Opinion Mining (English)
A generic sentiment analysis pipeline for English tweets. The pipeline identifies sentences containing basic positive and negative opinions, and includes basic entity detection. It creates an annotation for every opinionated sentence, with features denoting
- the polarity of the opinion (positive or negative)
- a score denoting the opinion strength
- a broad classification of the emotion expressed by the sentence
- an optional reference to the entity acting as the target of the opinion
- a feature denoting presence of sarcasm
- linguistic features such as whether the sentence is a question, conditional, imperative etc.
This pipeline is designed for use on tweets or short social media texts.
|Sentiment:Sentence||Sentences, with the following additional features:
|Sentiment:SentenceSentiment||Opinionated sentences, with features polarity (positive or negative) and score (from +1 to -1), plus sarcasm with value "yes" or "no" for sentences that are identified as sarcastic or not. There is also a feature emotion giving a broad classification of the emotion expressed by the sentence, possible values are "cute", "happy", "bad", "anger", "disgust", "fear" or "sadness".|
|Additional annotations available if selected|
|Sentiment:Person||Named entities as detected by TwitIE|
|Sentiment:Hashtag||Common twitter entities of hashtags, @user mentions and URLs|
|Sentiment:SentimentTarget||The target of a sentiment expression, with polarity, score and sarcasm features as described above.|
Use this pipeline
You can process up to 1,200 documents per day free of charge using the REST API, at an average rate of 2 documents/sec. Higher quotas are available for research users by arrangement, contact us for details.
The API endpoint for this pipeline is:
You can process any amount of data with this pipeline on a pay-as-you-go basis, for £0.80 per hour. This can be data you upload yourself, data you collected from Twitter, or the results of a previous job.