Sentiment Analysis Software Solutions

While humans are quite efficient at determining the sentiment of a statement, they can manually process only very small amounts of data, which severely limits any practical applications of sentiment analysis. Computers can process enormous amounts of data in virtually no time, but they need a lot of help in order to perform intelligent sentiment analysis. To obtain meaningful results, one needs to convey to the computer specific knowledge about the domain, context, negations, modality, emoji, and more.

What is Sentiment Analysis?

Challenge

Many important business tasks require the analysis of sentiment expressed toward the discussed objects. Here are some questions one can get answered through sentiment analysis:

What is the general sentiment (positive or negative) of the media coverage of an organization, brand, person, or legislation? What pros or cons are being brought up the most?
What features of the company’s offering are key drivers of negative sentiment among your customers?
How are different aspects of a product are perceived by customers, especially when compared with similar aspects of competing products?
What sentiment do market analysts have toward certain securities and how has it evolved over time?

Using Machine Learning for Sentiment Analysis

Megaputer provides a generic solution for sentiment analysis based on PolyAnalyst™ that can generate results of acceptable accuracy out of the box. To facilitate additional lift in accuracy, the solution can be customized to various domains by adding domain-specific cartridges for sentiment analysis provided by Megaputer. It can be further fine-tuned to a particular task by modifying the associated ontologies/dictionaries and adding task-specific SA queries. The desired enhancements can be carried out either by the customer or by Megaputer.

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How it works

Once the data is loaded, PolyAnalyst performs sentiment analysis capturing the evaluated object, the evaluation (sentiment), and the evaluation degree. The solution is able to detect and interpret separately multiple evaluations of different objects encountered in the same narrative. It can output sentiment on the level of the individual concept discussed, the entire text record, or the taxonomy category (when classification into multiple topics is carried out). The results of sentiment analysis become immediately available for further predictive modeling and/or reporting.