Sentiment Analysis Definition
“Sentiment analysis also called opinion mining. It is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes” as defined by Bing Liu in his book Sentiment Analysis and Opinion Mining.
There are also called by many different names and perform slightly different tasks such as sentiment analysis, opinion mining, sentiment mining, emotion analysis, opinion extraction, etc.
Brands understand the sentiment of their customers using sentiment analysis. What are people saying about your brand, what do they dislike, what improvements are they asking for, which new products are they looking for, etc. These customer sentiments can be found on social media and online forums in the form of tweets, likes, comments, and reviews.
Advances in deep learning have given sentiment analysis a foreground in cutting-edge algorithms. Today brands are using natural language processing (NLP) and text analysis to crawl data and identify the sentiment of text into positive, negative, or neutral categories.
Sentiment Analysis Advantages
To understand customer needs, it is important to leverage sentiment analysis. Some of the advantages of sentiment analysis are as follows:
1. Identify strengths and weaknesses:
Sentiment analysis, with the help of advanced technologies and big data, captures, quantifies, retrieves, and analyzes consumer behavior more effectively. Sentiment analysis identifies positive, negative, or neutral opinions of customers, which is used to determine the customer’s sentiment towards a brand or service. This helps brands to identify their strengths and weaknesses.
2. Formulate effective marketing strategies:
Sentiment analysis is used to study product performances, customer grievances, and to get in-depth information for strategic product analysis. Brands can design effective marketing campaigns with the help of sentiment analysis. Marketing campaigns can be evaluated using the ROI that can be estimated by assessing positive and negative opinions.
3. Improve product quality:
Sentiment analysis helps to gauge consumer needs and preferences. Product innovation and improvement ideas are offered by customers that can be an opportunity for product transformation. Sentiment analysis throws light on customer grievances, reasons for customer churn, which can be used to increase customer acquisition, improve customer retention, and handle customer grievances.
4. Better customer service:
Sentiment analysis brings awareness of the negative issues that are bothering the customers. A quick response to a customer issue will have a significant impact on keeping your current customers happy. When there are millions of users posting online, it isn’t very easy to engage with all of them. Sentiment analysis gives a concise view of all the issues and identifies those you should engage with to provide better customer service.
Sentiment Analysis Example
For example, in a study conducted by a student at Oklahoma State University, Amazon reviews were analyzed to find out brand preference. Samsung phone models (Galaxy S6 & Galaxy S7) and Apple (iPhone 6 & iPhone 7) were used for the study. It came out that customers who had a higher preference for a reliable battery and a good screen went on to buy Samsung phones. In contrast, iPhones were preferred by customers who are more attracted to design and camera.
Filtering comments by topic and sentiment, you can also find out which features are necessary and which must be eliminated. Armed with sentiment analysis, the product development team of an organization will know the features that customers are looking for.