Understanding Sentiment Analysis and its Use Cases


What is sentiment analysis?

Sentiment analysis, also known as opinion mining is a process of identifying and cataloging a piece of text according to the tone conveyed by it.This text can be comments, feedback, and even random rants. The sentiments related to the text can be positive, negative or neutral. Sentiment analysis can be done through Natural Learning Processing, computational linguistics or text analysis.  Extracting sentiments from the text can be considered as one of the greatest digital marketing services to individuals and business groups.


Sentiment Analysis is a layered process. A text can have many aspects. These can be information regarding semantics, syntax, and context. Consider this phrase- Put a sock in it! If you set basic rules, it might mean to put a sock to somewhere mentioned. When it actually means to shut up. Syntax information tells you what exactly is being said. And the context information tells you the specific context in which the text has been put. The combined analysis of all can be done through machine learning algorithms. Machine learning models have been used for the past few years for the purpose of sentiment analysis. But the recent years are witnessing the powers of deep learning. The traditional paradigms provide satisfactory accuracy but they have limitations and evolution is a necessity. Machine learning algorithms largely depend on the manually created features before the classification of the text is done. These features can be Lexical or Sentiment Lexicon- based. Other than that, these are also inclined towards corpus-based rules. This makes the system text- specific and questionable at times. Nowadays, text forms are more amorphous. Traditional approach can disappoint here. This gives the room for improvement and hence enters deep learning.
Deep learning rules out the need of manually crafted features. Sentiment analysis can be tricky. The advantage of using deep learning is that it adapts to the tasks variations while not involving major changes in the system itself. At ParallelDots, we have powerful sentiment analysis API that uses deep learning which provides an accurate analysis of the overall sentiment of the given text. The following image demonstrates how our sentiment analysis tool works.




The applications of sentiment analysis cannot be overlooked. If you doubt it, here’s a little outlook. The accuracy can never be 100%. And of course, a machine does not understand sarcasm. However, according to a research, people do not agree 80% of the time. It means that even if the machine accuracy does not score a perfect 10, it will still be more accurate than human analysis. Also, when the corpus is huge, manually analyzing is not an option.
Digital marketing is a very important part of the business. The reactions, the product gets on social media is important for the company. Sentiment analysis helps to determine the life expectancy of the product. It determines how the product performs in the market, how this performance can be improved or if it’s the time to stop the gig. It’s impossible and probably insane to detect who did not buy your product. But you can always look for the sentiments associated with your product. And also the response received by the competitors. If your product gets 25% negative reviews, it doesn’t necessarily mean that it is a bad thing or a good thing. It depends on the figure and reviews your competitor gets. That can be monitored through competitive analysis. Know more about how social media gives you the best competitive analysis in the article here.


Sentiment analysis has so much to offer to political affairs. Every significant election is entertained with utmost dedication by the people in general. Social media gets all swamped with politically drenched tweets, posts, and predictions which become inputs to the sentiment analysis tools. News channels make a good use of sentiment analysis technique to predict possible outcomes and keep the folks updated and entertained.
Political parties can also make a good use of sentiment analysis technology. They can always monitor the impacts of the political moves they make. For example, the central government’s recent currency demonetization orders generated reactions all across the country. In a machine learning analysis on currency demonetization, we analyzed overall sentiments of the people on the issue.

According to the pie chart, the majority of the people have appreciated the decision. So, the officials are now contented with the decision they have made now that they have their people’s consent. This helps to maintain the rapport between the citizens and the government in power.

Sentiment analysis in Finance

In the words of one of the most decorated investors in the world, “Be fearful when others are greedy and greedy when others are fearful”. But how do you know if others are fearful or greedy? Well, if you are aware of the sentiment analysis technique, you can have a fair idea. Making an investment is no rocket science but  tricky nonetheless. The market always involves risks but if you do some homework before investing, it can be condensed. Let’s take an example of the automobile industry. If you are confused about whether to invest in Company A or in Company B. Look for the sentiments received by their latest car models. You will know which one of these is performing better in the market. And now you know, which of these companies is worthy of your shares.

Sentiment analysis is more than just a trend. It has become a need. And it is indeed a very helpful technique to provide insights to implement an action. New methods are being researched and worked upon this subject to make it well grained.

ParallelDots is an ArtificiaI Intelligence research and Deep Learning startup that provides AI solutions to clients in multiple domains. You can check out some of our text analysis APIs and reach out to us by filling this form here.

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