Do you want to know what your customers are saying about you? Do you have problems extracting insights from raw data? Are you concerned about accuracy?
Sentiment Analysis is the answer to all those questions extracting topic, polarity, opinions and making them easy to understand and visualize.
There are two different approaches to achieve proper results on sentiment analysis: machine learning and deep linguistic analysis. At Bitext we use the second one because:
Based on grammars Deep Linguistic Analysis allows opinion analysis not only at the sentence level but also at the phrase level within the sentence. This is possible because of our in-house parsing technology. The syntactic analysis identifies the different phrases (noun phrases, adjective phrases, verb phrases, adverbial phrases) and their dependencies.
The sentiment analysis service is not limited to extract a single opinion per sentence. It can actually detect as many opinions as the sentence contains. As an example:
“This phone is awesome, but it was much too expensive and the screen is not big enough”
Three opinions will be extracted: “phone” + “awesome”, “phone” + “much too expensive” and “screen” + “not big enough”.
The sentiment analysis service handles also complex language structures which play a major role in sentiment analysis, such as negation or comparative sentences. Deep Linguistic Analysis automatically handles these structures and can capture the difference between opinions like:
“This phone is much better than my old phone.” - Positive
“This phone is not much better than my old phone.” - Negative
We don’t focus on just telling you if something is positive or negative. We go beyond that and show you what is the opinion is about. That is our sentiment topic. This is really useful to quickly find problem areas in your business and to get really granular results without reading the original texts.
For example, “The service was horrible but the food was absolutely brilliant.” we have two different opinions about two really different points. The sentiment topics are: “service” and “food”.
Sentiment scoring is also based on Deep Linguistic Analysis. The more intense the feelings of the person about the subject, the higher or lower the score.
To achieve this, the linguistic analysis detects linguistic features such as the semantic strength of the vocabulary or the use of intensifiers like “really”, “very” or “extremely”.
For example, “Installing software on this machine is painful!” will be scored as less negative than “Installing software on this machine is really very painful indeed!”
People talks and they like to express their opinions in public. When it comes to a marketer this is a great source to get data and turn it into insights. However, analyzing many comments from Twitter, for example, can be time-consuming: analyzing the phrases, categorize them depending on the topic, what do they say etc.
If you want to save time and deliver actionable insights download our presentation
When combined with our Categorization Service, the sentiment analysis service can assign features or attributes of the topic to categories from a taxonomy. This combination provides a powerful way to structure a set of texts according to what topics people are discussing and how they feel about those topics.
Our cloud services help market research professionals and data scientists perform sentiment analysis, categorization and entity & concept extraction, easily and effectively.
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