What Is Sentiment Analysis?.

What Is Sentiment Analysis?

Keeping a finger on your customers’ pulse has always been the key in driving successful business enterprises. Understanding what users feel about your products and services, implementing the changes requested, solving the problems faced, coming up with new means to reach them better can be crucial while taking the next step to understand customer mindset

One of the best ways to gain considerable consumer insights is through sentiment analysis. Sentiment analysis has been around for a few years but because of the lack of real time data and deep learning tools, it stayed in the backseat. However, with abundant data availability and various deep-learning models at disposal, Sentiment analysis is again gaining momentum. So what exactly is sentiment analysis? What technological infrastructure is it backed by? And what can sentiment accuracy do for your business? Let’s explore answers to all these questions.

Sentiment Analysis?

Sentiments, alternatives for feelings, attitudes, opinions, advices etc. can be a handful to handle. Sentiments can be defined as various subjective impressions a person can have about what he or she is exposed to. So what has a business got to do with this? In business terms, we can relate this to comments, reviews, discussions, suggestions, or experiences your customers have when experiencing your products and services.

Sentiment analysis deals with analyzing all that the customers have been talking about your brand, be it positive or negative. Every word that a consumer puts out on any media platform can serve as a means to understand the feelings of the user. Often used together, words like semantic orientation and polarity, sentiment analysis entails identifying and characterizing user content by means of natural language processing, advanced machine learning methods and statistical tools. It is otherwise referred to as opinion mining too.

Which Questions Can Sentiment Analysis Answer?

From finding whether a product review is positive/negative to grasping the acceptance of your new marketing campaign, sentiment analysis can give you all the answers hidden in a simple text. It broadly comprises of,

  1. Information extraction: This enables discarding the relative information and mining the right content for you.
  2. Flame detection: Analyzing the nature of negative content being put out regarding your brand or any specific offering you have in the market.
  3. Polarity Detection: Enables you to associate specific keyword with the reviews and comments generated and identify polarity if any exists.
  4. Classifying: This guides in sorting out the good and bad content and studying the nature of words used in it, usually speedup by counting the number of positive and negative adjectives used.


Conducting continuous and productive sentimental analysis can throw a light on what your customers like and do not like, their preferences, specific things they would like to change, their experience with the service delivered and if they will recommend your services and products to anyone and or buy again from you et al. Creating a data set from these answers and running continuous analysis enables good feed to business intelligence.

Techniques Used

Although there are various tools and software which are readily available to conduct sentiment analysis but accuracy is the key. A weak sentimental analysis can not only give insufficient clarity but also misguide the conclusions derived due to inability to understand the polarity well. Sentimental Analysis is a text classification, which largely works on a machine learning mechanism with specific algorithms.

Supervised: When this technique is used, the machine already has a certain classified data set with which it takes reference of, to classify text mainly into positive, negative and neutral polarity. Popular models here include the maximum entropy model classifier and support vector machines.

Unsupervised: Here as there is no prior data set for reference the working popularly used is part of speech tagging. Once the words are extracted, semantic orientation is estimated, cumulative semantic orientation of the collected phrases helps in classifying.


Sentiments are generally expressed as opinions and are subjective to the user and not facts. It is fairly obvious that machine-learning systems used cannot exactly interpret what a human being wants to express. As the way people express feelings in words can be tricky calling for the need for a strong sentiment analysis model. Let’s look at the challenges experienced while conducting sentiment analysis,

  1. Taking literal meaning of content can lead to problems, as opinion texts are complex to interpret. They can comprise positive and negative things together and one positive thing does not indicate a positive classification thus, only lexical analysis can be misleading.
  2. Humor and Sarcasm is extremely difficult for a machine to understand unless it has been trained under tremendous data sets of such data and has undergone many simulations. But now as deep learning is evolving this problem has become relatively easier to tackle.
  3. Predispositions on the side of a business enterprise can lead to analyzing only that data, which can feed to the hypotheses, they have in their mind. On the other hand keeping an open mind enables you to see the problems from a more holistic view and understand that things may not be the way you had predicted. This in turn opens up new findings from the data to improve on.

How Important Is Sentiment Accuracy?

Sentiment accuracy refers to how accurately can you interpret the feelings of your user base or analyze the conversations you are a part of, on the web. Sentiment accuracy is the core of any sentiment analysis, as without higher rates of accuracy conducting sentiment analysis seems useless. There are various techniques by which you can work hard on increasing the accuracy of your analysis and they largely depend on obtaining the right data. Right data can only be facilitated by the use of right information extraction tools, cleaning and classifying this data will be the next step. Using good semantic orientation tools, which can score individual phrases and words, does not guarantee accuracy. The other way by which you can increase accuracy is exposing your ML model to extensive training by feeding it with large variety of data.

Finding the right tool or opting to conduct it in-house is a decision which is crucial as, you may find the right tool or not and doing it in-house may be costly for some and not for some. At the end of the day the deciding factor should be the accuracy obtained by the models and a fair call based on accurate results. Higher levels of accuracy will add value to your strategic decisions and help in creating value for your consumers further.

For a complimentary, no-obligation discussion around ideas on how to measure customer sentiment for your enterprise and your industry sector, please get in touch with us now. 

Leave a comment

Your email address will not be published. Required fields are marked *