An Overview of Sentiment Analysis

When it comes to online shopping, many among us consider the product reviews before making a decision. As customers, if someone else’s feedback can influence our choices, how much more will these reviews matter to the ones running the business? The emotional tone behind the customers’ expressions can determine the polarity (positive, negative, or neutral) of their opinion. Identifying and categorizing such subjective information using natural language processing-based techniques is Sentiment Analysis. It can help organizations to gauge how well they are doing in the market and also to work on the areas that need attention. Sentiment analysis has applications in different domains like business intelligence, politics, sociology and so on. 

Why does it matter?

Companies can have a lot of valuable data that is unorganized like emails, chats, social media, surveys, articles, support tickets, and documents. Manually sorting through is difficult, time-consuming and expensive too. Leveraging sentiment analysis enables automation which makes it easier to gain actionable insights. It can process vast amounts of information efficiently and at low-cost. One can also identify and mitigate a potential crisis.

So, how does it work? Using machine learning algorithms, we can train a model to learn from the past data, so that it can predict an output whenever a new data point comes in. The more data we use to train the model, the more accurate it becomes. Although these algorithms are designed by human intelligence, they are optimized by the automating power of computers.

Some Applications of Sentiment Analysis

Sentiment Analysis has the potential to revolutionize the way business works. The applications are plenty; let’s see how it can be used in Market Research Analysis, Social Media Monitoring and Product Analytics.

Market Research and Analysis

1. Real-time analysis for quick mitigation of risks
2. Analyze and compare product reviews with the competitor’s product reviews
3. Gain quantitative insights from qualitative information
4. Analyze reports and journals for long-term trends

Social Media Monitoring

1. Run sentiment analysis on social media to gain insights into a company’s brand
2. Analyze posts to study the sentiments of a target audience
3. In case of high priority, route social media posts to the corresponding team handling them

Product Analytics

1. Categorize and filter reviews by emotions and relevance
2. Analyze volumes of feedback surveys
3. Automatically route messages to product teams
4. Review online mentions of the product

The valuable information sought from huge datasets can overshadow the apprehensions about manually analyzing data as separate entities. Sentiment Analysis can facilitate making results interpretable and actionable. Learn more about Sentiment Analysis from the various articles on J-Gate.