Sentiment Analysis

Sentiment Analysis

Sentiment analysis is that the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback.
With the recent advances in deep learning, the pliability of algorithms to analyse text has improved considerably. Creative use of advanced computing techniques could also be an honest tool for doing in-depth research. We believe it is important to classify incoming customer conversation a pair of name supported following:
1. Key aspects of a brand’s product and repair that customers care about.
2. Users’ underlying intentions and reactions concerning those aspects.
Also sentiment analysis is most common text classification tool that analyses incoming messages, social posts, comments on forum, etc. which is believed as Intent Analysis Or Profanity Analysis

What does it do ?

Sentiment analysis model detects the polarity within a text (positive or negative), understanding people's emotions is incredibly important for any business, since users can express them themselves in reviews more freely than ever.
For example : A owner of a business used sentiment analysis on the reviews given by the purchasers and located that the bulk of the purchasers were happy by his product, as you will see within the image below.

Now look at the figure below, as you can see it can also tell the emotion attached to comments by analyzing the sentences

Types of Sentiment Analysis

If the polarity is very important to the owner, then most of the emotions should be like
1. Very Good

2. Good

3. Neutral

4. Bad

5. Very Bad.

Here, Very Good = 10 points, and Very bad = 1 point

This same kind is used in one of our college feedback forms, which is pretty good, as the it directly tells the guardians and faculty member weather we are happy or not...

How does this work?

so you would possibly be wondering by now that how did this work?
The process is pretty simple as followed :
1. Break each text document down into its component parts (sentences, phrases, tokens and parts of speech) (Bag of Words)
2. Identify each sentiment-bearing phrase and component (Dictionary meaning and in context meaning) (Lemmatisation)
3. Assign a sentiment score to every phrase and component (-1 to +1)(Can be of any range as an example above is 0 to 10)
4. Optional: Combine scores for multi-layered sentiment analysis. (For system which has multiple output like "Very Good", and "Good"

Based on these points train the model.  There will be 3 types on this :
1. Rule-based systems that perform sentiment analysis supported a group of manually crafted rules.
2. Automatic systems that depend upon machine learning techniques to be told from data.
3. Hybrid systems that combine both rule-based and automatic approaches.

Where do we use it?

1. We can use it to detect the customers reviews in any business 

2. As for now majority of the social site like facebook and twitter use this to check the person behavior on the site.

For now there are many database available online in which you can some of your own words and train it. There are ready made pre-trained open source models on GITHUB, which you can use for your project.


Thank you for reading this.

If you got any doubts? please feel free to ask in the comments below. we will get back to you asap

By Kapil Kadadas