What are the Tools and Techniques of Text Analytics?

There is a plethora of unstructured data available on the Internet and making its entry into the customer call centers. However, going all manual on the haystack of data to find the needle is simply an unrealistic task to accomplish. Here are some of the tools and techniques of text analytics used by Provalis Research:

Sentiment Analysis

Analysis of people’s tone or opinion about your organization on social media or on your call centre can help you address the issues faster and notice how your products and services are performing in the market etc.

Sentiment analysis can be carried out in three ways:

  • Polarity analysis: identification of tone of communications as positive or negative
  • Categorization: tools are more fine-grained and
  • Identifying if customers are angry or confused
  1. Topic Modelling

It is a useful way of identifying dominant themes in a plethora of documents and for dealing with the same amount of text. Topic modelling is super helpful in legal firms.

Topic modelling can be implemented in two ways:

  • Latent Dirichlet allocation: words are automatically clustered into topics with a concoction of topics in each document.
  • Probabilistic latent semantic indexing: models co-occurrence data with the help of probability
  1. Term Frequency – Inverse Document Frequency

Term frequency — Inverse Document Frequency checks how repetitively a word makes an appearance in a document and its importance relative to the whole set of documents.

You can adjust the weights on the basis of the frequency the terms appear in a particular document set. Then take those features so that the words that show up in a frequency in a particular set and they will be good indicators. These weights are what the machine learning algorithm uses to implement the classification.

  1. Named Entity Recognition

Named Entity Recognition basically focuses on recognizing the nouns and are useful in extracting people, organizations, geographic locations, dates, monetary amounts, or the like from text.

It also performs pattern recognition. This really comes in handy if you have your own specialized sets of terms you want to search for and they are available in a varied structure and you are able to train it as well. One of the best algorithms to implement this is termed as conditional random fields. Normalization of words also comes in handy with the NER. To be consistent in its implementation, to make the analysis easier, and to make the words recognizable as well, the abbreviations are filled with their generic names. But, these abbreviations can turn out to be notoriously ambiguous which makes the task of normalization tougher. The whole practice of using the NEW technique involves crucial data preparation and training which can turn out to be time and effort consuming.

  1. Event Extraction

It is deemed to be the next level implementation of NER but tougher to implement. It doesn’t only look at nouns or what is being talked about, but the existing relationship between them and the types of interferences that can be drawn out from the incidents referred to in the text.

Post Author: Wyatt Canton


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