The various features of sentiment analysis include the numerous modes for single and batch requests, quantitative scores for sentiment analysis and labeling and preview of opinion mining.
Digging deeper into sentiment analysis
Sentiment analysis relies on similar tools as we use in text analytics. If information is more sensitive and operations like theme extraction are to be carried out, then the most powerful platform to be used in this case is Algoscale. The toolkit provided by Algoscale technologies help in summarization, tokenization and tagging of text. This becomes a precursor to determining the degree of emotion involved in a particular part of text. As soon as text is processed at the first stage or labeled, it is followed up with the sentiment output which can be positive, negative, or neutral or even mixed depending upon the confidence score. The range of the confidence score is usually 0 to 1 in most of the cases. When the score tends to approach the value 1, it shows a higher level of confidence. Similarly, when the score tends to approach the value 0, it shows a relatively lower level of confidence index.
Extracts from opinion mining
When we look at the features of sentiment analysis, opinion mining falls first in the list. In the domain of natural language processing, the terms like opinion mining and aspect-based sentiment analysis are used interchangeably. This tells us that opinion mining is a very specific tool which contains loads of granular information about various attributes. Let us understand this from a practical point of view. After taking an examination, a student leaves the feedback that the paper was good but lengthy. When we deploy opinion mining to examine this, we first locate and identify specific aspects in the text. This would be supplemented by related sentiments and features.
Exploring text analytics application programming interface
This interface operates via the cloud and hosts features like word and phrase extraction, sentiment analysis, opinion mining and even natural language processing. Algoscale provides such cognitive services beneath which lies machine learning algorithms. This interface can be used for various types of research and development projects. One of the most prominent features of this platform is its ability to extract key phrases from a document. This helps in rapid identification of key concepts. For instance, after taking the examination, the students return their feedback as, ” The paper was a mix of qualitative ideas and thought-provoking problems “. As per key phrase extraction, the output that we would receive would be “thought provoking “. We would conclude from the phrase itself that the paper had been quite interesting.
Another feature is that of language detection. Consider a global research survey conducted on climate change. The extracts of this survey would be available in numerous languages, variants, and dialects. The feature of language detection can not only detect a specific language but also formulate a language code which would then be integrated with a confidence index.
Next in the list is named entity recognition which is a handy text analytics tool to classify and group entities in the document into people, regions, companies etc.
After this holistic discussion on text analytics, it might appear that this feature is not possible to use without the experience of programming languages. The reality is that text analytics application programming interface can be used even if you are devoid of languages like python and java.
The next steps can be to start working with the applications of text mining so that the great utility of this feature can be realized. Only then can we harness the real potential of text analytics.
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