Why Is Sentiment Analysis Important

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Every company wants to understand their customer. In the past, surveys were required to get an idea of how customers felt about products or services, but today, there are many conversations about brands and companies online. Although most of these resources are available to anyone, combing through a huge amount of data can drain time and resources. Sentiment Analysis speeds up the process of retrieving data with automation and refining insights. 

What Is Sentiment Analysis?

Sentiment Analysis uses automation to evaluate texts and detect consumer sentiment implied in them. Rather than reading reams of text, this process employs machine learning and algorithms and categorizes the tone of texts as positive, negative, or neutral.

Another term for Sentiment Analysis is opinion mining and involves monitoring web pages to learn more about customers and competitors. The most common data sources for consumer sentiment are social media pages, online reviews, discussion platforms, and places people regularly express opinions about products and brands. 

Of course, reading all of this material would be too time-consuming and inefficient. Machine learning tools are the key to opinion mining. But can these tasks be automated? Can machine learning replicate the human ability to analyze texts for attitude and tone?

Using the Right Tools for Sentiment Analysis

With sentiment analysis, as with many other pursuits, quality makes a difference. Advanced machine learning tools can sense nuance of words, degree of enthusiasm or disapproval, and even sarcasm. 

For example, the sentence in a review of a dry cleaner may read, “My clothes were clean and looked alright, but the service wasn’t that great.” The tool will analyze the first part of the sentence as a positive and the second part of the sentence as a negative. Even though the word “great” is used, the tool will be able to combine the word with “wasn’t” after consulting a sentiment library that can identify all parts of speech and evaluate them for tone. Appropriately, machine learning would interpret this review as mixed with both positive and negative elements. 

However, it takes advanced automated tools to detect the precise tone of reviews or comments. Sarcasm is a common example. In real life, we know people are sarcastic by their tone of voice, but online, context is the key to understanding irony. Some automation can be sophisticated enough to detect the sarcasm in the comment, “They brought me my hamburger but not my onion rings. Great Service.” 

A sentiment analysis tool will detect gradations of feeling and can distinguish between very enthusiastic and pleased or disappointed and upset. 

The Process of Sentiment Analysis

The following steps describe the process of Sentiment Analysis.

  • Gathering Data

The first step to conducting sentiment analysis is gathering data. Frequent sources are social media pages, review sites, eCommerce platforms, and other websites. One of the best ways to retrieve texts is to use web scraping with a proxy to disguise your IP address. Once the data is downloaded into a file, it can be analyzed. 

  • Clean Up Text

Before evaluating text, stop words, such as conjunctions and punctuation marks, should be removed. Text cleaning tools are used to do this job and prepare text so sentiment analysis tools can evaluate it.

  • Sentiment Analysis

Once the text is prepared, algorithms are used to evaluate the data. Advanced tools can spot nuances between positive and negative tones. A sentiment library is used to interpret words according to connotation and mood automatically. 

  • Surveying the Results

Sentiment Analysis tools provide feedback on the feelings your customers are expressing about your product or service. It is useful to have a date for the data to track trends, including peaks and valleys, so that you can interpret them according to certain events or promotions. 

Applications of Sentiment Analysis

Sentiment Analysis has many applications. The following are the most common and useful. 

  • Social Media Monitoring

People spend a large amount of time on social media. Given how often customers at a restaurant take pictures of their entrees and share them on Facebook or discuss their shopping experiences on Instagram, it is clear that social media platforms are a treasure trove of consumer sentiment. Data from social media can help refine general strategies as well as addressing the complaints of specific customers. 

  • Customer Reviews

The majority of consumers who shop online read reviews before making a purchase. Scraping pages from review sites where your product is discussed can give you a strong indication of how consumers feel about your company. Responding to negative reviews can help change the minds of individual customers and can forestall a negative impression. Monitoring your brand presence across the web, analyzing sentiment, and responding to it is a winning strategy. 

  • Customer Support

Customer support through chatbots or live chat can provide an opportunity for honest feedback through direct communication. Performing sentiment analysis on chat records can reveal what needs to be fixed in product offering, marketing, and customer service. Encouraging customers to leave feedback immediately after interacting with a company representative can provide valuable, candid views about your service.

Keeping Tabs on Your Customer

Sentiment Analysis adds context to evaluating metrics such as sales and revenues. The latter numbers provide the “what,” but Sentiment Analysis can answer the question of why your sales are not as strong as expected or why your revenues rose last month. Using advanced sentiment analysis tools adds meaning to what your customers say about your brand and provide a full picture of how your company is viewed. 

Why Is Sentiment Analysis Important


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