What is Sentiment Analysis

What is Sentiment Analysis

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Everyone of us has expressed an emotion when we take our time to describe an experience to our friends and family. Often now in our sharing economy, we find ourselves constantly sharing these experiences across digital platforms like social media platforms and online review sites. With so much feedback being generated regarding experiences with products and services, it is useful for companies to work to understand these emotions of their customers. To do so, companies are increasingly turning toward Sentiment Analysis as a tool to better understand their customers so they can offer better experiences.

Sentiment Analysis (also known as opinion mining) refers to the use of (NLP) natural language processing, (ML) Machine Learning techniques, and text analysis to identify, extract, quantify, and study the feelings and emotions that are conveyed in written text.

To put it plainly, Sentiment Analysis is used to determine if a piece of writing is positive, negative, or neutral.

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How Does Sentiment Analysis Work?

The basics of Sentiment Analysis follows the following steps:

  1. Break each written document down into its shorter snippets, or its component parts. This is typically done by separating sentences, specific phrases or other similar parts of speech.
  2. Identify each sentiment-bearing snippet or phrase of the text. This is often done by matching it against a library of topics you are keen to identify and measure.
  3. Assign a sentiment score to each snippet to indicate positive or negative emotion.
  4. Optional: Combine and visualize these scores for greater understanding of sentiment trends across thew whole document or a series of documents.

To better visualize this, consider the following sentence (i.e. a review of an amusement park):

“We had a great time at the amusement park, the food was great, but we hated standing in lines”

If we break the above sentence into smaller snippets for sentiment understanding we might separate it like this:

“We had a great time at the amusement park, | the food was great, | but we hated standing in lines”

Based on the above breakup we were able to identify that there were three major elements of the sentence that the reviewer referred to. So, if we take each section, we can apply a sentiment value to each snippet that relates to the topic mentioned – like so:

Amusement Park = Positive

Food = Positive

Lines = Negative

By using the adjectives used to describe the emotion around each of these specific elements, we are able to identify the basic sentiment of each aspect of the review.

Ultimately the process is very simple, however when applied to massive amounts of data like social media exports or survey responses, the task can be quite large. This is where advanced technologies and algorithms are brought in to automate the above processes to achieve the results at scale.

To learn more about the various technological processes and algorithms used in Sentiment Analysis, please access some of the resources here for further study:

what is sentiment

How is Sentiment Analysis Used?

We see Sentiment Analysis commonly used in (VOC) Voice of the Customer applications and processes as a means of applying a measurable element or metric, to vast quantities of text commentary from sources like:

  • Social Media Mentions
  • Online Reviews
  • Survey Responses
  • Customer Support Inquiries
  • More…

We find that when customers are offering products or services directly to an end consumer, then the likelihood that their customers are providing feedback with the company directly, or indirectly through social media on online reviews, is very high. Especially with companies that deal with a lot of customers, they will often find that the amount of feedback they receive is proportionately large.

Sentiment Analysis is used as a staple of modern VOC (Voice of the Customer) technologies as a means of helping companies transform huge volumes of customer feedback from multiple sources, into clear and simple to understand insights and trends into how their customers feel about their products or services.

Why is Sentiment Analysis Important?

Customer Experience (CX) is vastly becoming one of the most important aspects of a company’s wellbeing. In todays’ sharing economy, we are seeing an astounding number of people sharing their thoughts and experience with their friends and peers, and in turn making a direct impact on purchasing decisions and company bottom lines.

In order for companies to make sure they are getting to the root of what their customers want, it is imperative that they know how their customer feels.

Sentiment Analysis is a key component used by companies to understand and depict customer emotions when at scale. Without technology like Sentiment Analysis, to achieve the same result and understanding, there would need to be a dedicated annual effort by one or many staff members to read and score every piece of customer feedback one by one. However, with the miracle of modern-day Sentiment Analysis techniques, this can be done programmatically, and the time of your staff can be spent identifying data-supported actions they can take to improve the experiences of customers.

How does Tatvam Help to Analyze Customer Sentiment?

At Tatvam, we help you listen to your customers better. We have a unique rating system in our platform called the Tatvam rating, which shows you the customer sentiment of any comment or mention of any specific topic with unparalleled accuracy.

Sophisticated and advanced Sentiment Analysis algorithms and programs process and store large amounts of data, which are then transformed into actionable insights for you to use to make better decisions regarding your customer experience (CX).

Because the one sentiment we want to leave you and your consumers with is happiness.

Topic-Wise Sentiment Scores

Instead of focusing on general analysis, we have a more topic-based sentiment analysis process which allows you to measure sentiment of the specific mentions of any unique elements of your customer experience. The topic-based analysis gives specific scores, thus letting particular sections within an organization know where to improve and what to change in order to improve or mellow out the sentiment.
By tracking the scores of topics/sections continuously and observing trends, it becomes super clear and helps in understanding customer experiences to a much deeper level.

Competition Sentiment Tracking

For a successful business, it is equally important to know the kind of customer sentiment shown towards competing brands. For example, the sentiment about a salespersons’ friendliness of company A is more positive as compared to that of company B, but B scores better in price related sentiment as compared to A. In short, if you are A, people like your employees but not your prices. This can help companies direct attention and resources in a controlled and systematic manner instead of random and haphazard action execution. Tatvam provides topic wise insights by deriving sentiment information from public reviews received by competitors, thus helping you gain an edge over the competition.

As said earlier, customer experience (CX) drives business growth, and that makes Sentiment Analysis really vital. If you haven’t opted for customer sentiment analysis yet, it is high time you did, and with the correct tools, Tatvam is here to help you!

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