Category : Text Mining

Information Retrieval Machine Learning Publications Text Mining

Sentiment Analysis

The customers’ sentiments and opinions are key information in marketing. Feedback about the items customers have bought can be used to optimize production. On the other hand, in a political context, the population’s opinions about a given law are crucial for its establishment. In the field of advertising, knowledge about customers’ sentiments can be useful to refine the parameters of campaigns to achieve better targeting. We can imagine that being in possession of a tool which can measure this “sentiment” is an invaluable asset.

Sentiment analysis, as its name explicitly suggests, is a system that automatically identifies a sentiment in a multimedia (audio, video, text) document. The fields of application are very diverse: commercial products, political law, event, etc.

The large amount of rich textual information available on the internet thanks to the numerous websites (social networks, e-commerce site, news sites, etc.) is a tremendous aid in designing a sentiment analysis system.

We will focus on textual document sentiment analysis. Even if the document that contains the opinions is not textual, an ASR system allows the opinion to be obtained in textual format. Here we present a sentiment analysis based on a Naive Bayes classifier.

Read More
Information Retrieval Publications Text Mining

Text Mining for Information Retrieval

Text mining aims at determining the similarity between a given text and a set of documents stored in a given database. In Information Retrieval (IR), Text mining can be used to extract essential information in any textual query document.

Read More