What is sentiment analysis?

A simple Google search tells us that it is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutralThis is actually a part of NLP, but it tackles a slightly simpler and a more specific problem. In this case, you do not have to go on and understand an entire text or recording. You just have to classify the text. Classification can be based on the required possible outcomes. You might need to classify tweets as happy, sad, angry, none or comments as positive, negative or neutral.

What is the difference between normal datasets and datasets for sentiment analysis?

For sentiment analysis you would generally be using text or voice recordings. For simplicity, let’s just go with text. You would be needing all your texts to be of similar lengths (preferably smaller). Some places where you can find these kinds of data – tweets (because all of them are 280 characters), product reviews on websites like Amazon (since they have some sort of word restrictions in place), company review websites like Glassdoor (since their data is structured with cons and pros in a way that makes it very easy to use).

How can sentiment analysis be performed easily?

Sentiment analysis can be pretty simple once you identify the words that matter. Say you make a list of words that are positive, and a list that is negative. Now you use this list in order to find similar words in text and build a frequency distribution. This can be used to make a comparative study and declare whether a piece of text is positive or negative.

Where can one get datasets for sentiment analysis?

You would not find datasets for sentiment analysis in public domain although all the data such as reviews and comments are indeed publicly viewable. The publicly viewable data has to be scraped using web scraping techniques and arranged into a proper format so as to make it consumable for intelligent systems.

Why is Sentiment Analysis so important today?

Sentiment analysis is important to maintain brand name and  image. Although your product might be best in the market, a bad PR stunt or false rumours, or disgruntled customers can cause massive outrage on social media, leading to a fall in your stocks and ultimately devaluation of your company. Keeping a watch on the internet for latest news or reviews or articles with mentions of your brand name and then subsequent evaluation of the text to find whether it is positive, or negative and whether it poses a direct threat, can help you nip such problems in the bud by directly interacting with the concerned party and reaching a favorable conclusion.

With so much data available in the internet that be used for sentiment analysis, all you need is a team like us at DataStock. The datasets offered by us have been arranged in a single place, created for sentiment analysis, and ready to used by ML algorithms for training. Strong ML algorithms help you analyze data at a faster rate in an accurate manner, so the response to reputation management and branding issues can be swift.