Clean noisy texts through stopword removal, parts-of-speech tagging and lemmatization.
Visualize topics and sentiments in intuitive ways.
Accelerate your literature review and
automatically analyze the content of papers
Analyze offical reports published by large organizations, e.g., financial reports, corporate responsibility reports, or corporate governance reports
Track the development of topics in the news
Listen to conversations on online social networks,
forums, blogs and other social media channels
Mine the content and sentiment of
product descriptions and customer reviews
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Analyze any text stored in
Excel, JSON or TXT files
The idea behind topic modeling is that words that co-occur together in similar contexts tend to have similar meanings. Hence, sets of highly co-occurring words (e.g., ball, pitch, goal) can be interpreted as topics (e.g., football) and used to cluster documents into thematic categories. LDA is a popular topic modeling algorithm that is able to discover topics running through a large collection of documents and to annotate individual documents with topic labels. As an unsupervised machine learning algorithm LDA is purely data-driven and inductively infers topics from given texts â neither necessitating any manual labeling of documents, nor the existence of predefined categories.
Learn more Papers using LDASentiment analysis deals with the quantitative measurement of opinion, attitude and subjectivity in texts. The SentiStrength algorithm estimates the strength of positive and negative emotions expressed in short texts. SentiStrength follows a dictionary-based approach to sentiment analysis and, therefore, can operate in many different domains. Besides relying on a dictionary of words with human sentiment polarity and strength judgments, it also exploits other (non-lexical) information, such as, negation, booster words, idioms, emoticons or punctuation.
Learn more Papers using SentiStrengthFrom researchers for researchers
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