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Twitter Sentiment Analysis – Introduction and Techniques

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Sentiment Analysis is a technique widely used in text mining. Twitter Sentiment Analysis, therefore means, using advanced text mining techniques to analyze the sentiment of the text (here, tweet) in the form of positive, negative and neutral. Twitter Sentiment Analysis, also known as Opinion Mining, is primarily for analyzing conversations, opinions, and sharing of views (all in the form of tweets) for deciding business strategy, political analysis, and also for assessing public actions.

Enginuity, Revealed Context, Steamcrab, MeaningCloud, and SocialMention are some of the well-known tools used for Twitter Sentiment Analysis. R and Python are widely used for sentiment analysis dataset twitter. Sentiment Analysis of Twitter data is now much more than a college project or a certification program. A good number of Twitter Sentiment Analysis Tutorials are available for educating students on the Twitter sentiment analysis project report and Twitter sentiment analysis using R and Python. You may also enroll for a Twitter sentiment analysis python tutorial for a promising career in sentiment analysis dataset twitter.

Our discussion will include, Twitter Sentiment Analysis in R, Twitter Sentiment Analysis Python, and also throw light on Twitter Sentiment Analysis techniques and teach you how to generate Twitter Sentiment Analysis project report, and advantages of enrolling for a Twitter Sentiment Analysis Python Tutorial.

What is the Twitter Sentiment Analysis?

Sentiment Analysis is a technique used in text mining. Twitter Sentiment Analysis may, therefore, be described as a text mining technique for analyzing the underlying sentiment of a text message, i.e., a tweet. Twitter sentiment or opinion expressed through it may be positive, negative or neutral. However, no algorithm can give you 100% accuracy or prediction on sentiment analysis.

As a part of Natural Language Processing, algorithms like SVM, Naive Bayes is used in predicting the polarity of the sentence. sentiment analysis of Twitter data may also depend upon sentence level and document level.

Methods like, positive and negative words to find on the sentence is however inappropriate, because the flavor of the text block depends a lot on the context. This may be done by looking at the POS (Part of Speech) Tagging.

Why Twitter Sentiment Analysis:

Sentiment Analysis Dataset Twitter has a number of applications:

Business: Companies use Twitter Sentiment Analysis to develop their business strategies, to assess customers’ feelings towards products or brand, how people respond to their campaigns or product launches and also why consumers are not buying certain products.

Politics: In politics Sentiment Analysis Dataset Twitter is used to keep track of political views, to detect consistency and inconsistency between statements and actions at the government level. Sentiment Analysis Dataset Twitter is also used for analyzing election results.

Public Actions: Twitter Sentiment Analysis also is used for monitoring and analyzing social phenomena, for predicting potentially dangerous situations and determining the general mood of the blogosphere.

How to Perform Twitter Sentiment Analysis:

  • Twitter Sentiment Analysis Python:

Twitter Sentiment Analysis Python can be done through popular Python libraries like Tweepy and TextBlob.

Tweepy: Tweepy, the Python client for the official Twitter API supports accessing Twitter via Basic Authentication and the newer method, OAuth. Twitter has stopped accepting Basic Authentication so OAuth is now the only way to use the Twitter API.

Tweety gives access to the well documented Twitter API. Tweepy makes it possible to get an object and use any method that the official Twitter API offers. The main Model classes in the Twitter API are Tweets, Users, Entities, and Places. Access to each returns a JSON-formatted response and traversing through information is very easy in Python.

TextBlob: TextBlob, one of the popular Python libraries for processing textual data, stands on the NLTK. It works as a framework for almost all necessary tasks, we need in Basic NLP (Natural Language Processing). TextBlob has some advanced features like –

  • Sentiment Extraction
  • Spelling Correction

 TextBlob is useful for Twitter Sentiment Analysis Python in the following ways:

 Tokenization:

TextBlob can tokenize the text blocks into different sentences and words. This makes reading between the lines much easier.

Noun Phrases Extraction using TextBlob:

The noun is mostly used as an Entity in sentences. It is also one the most important NLP utility in Dependency Parsing. This is how different nouns are extracted from a sentence using TextBlob –

Part-of-Speech Tagging using TextBlob:

TextBlob is also used for tagging parts of speech with your sentences. For example –

 

Part-of-Speech Tagging with TextBlob

 

N-Grams with TextBlob:

Here N is basically a number. N-Gram is basically a chunk of words in the group. For a deep understanding of N-Gram, we may consider the following example-

TextBlob

 

 

Twitter Sentiment Analysis in R

R, a programming language intended for deep statistical analysis, is open source and available across different platforms, e.g., Windows, Mac, Linux.  You can use R to extract and visualize Twitter data. You can create an app to extract data from Twitter.

Prerequisites for creating an app for extracting data for Twitter Sentiment Analysis in R

  • R must be installed and you should be using RStudio.
  • In order to extract tweets, you will need a Twitter application and hence a Twitter account. If you don’t have a Twitter account, please sign up.
  • Use your Twitter login ID and password to sign in at Twitter Developers.

Follow the steps below:

Once you have your twitter app setup, you are ready to dive into accessing tweets in R

You will use the retweet package to do this.

Twitter Sentiment Analysis with R

The first thing that you need to set up in your code is your authentication. When you set up your app, it provides you with 3 unique identification elements:

  1. appnam
  2. key
  3. secret

These keys are located in your twitter app settings in the Keys and Access Tokens tab. You will need to copy those into your code. Next, you need to pass a suite of keys to the API.

Finally, you can create a token that authenticates access to tweets! Note that the authentication process below will open a window in your browser.

Tools for Twitter Sentiment Analysis:

Enginuity:

Enginuity, even though a paid solution, a basic version is available as a free web application. It works differently from many of the free sentiment analytics tools out there. Instead of directly querying tweets related to a certain keyword, Enginuity allows you to search for recent news stories about the keyword.

The tool then queries both Twitter and Facebook to calculate how many times the story has been shared. It also analyzes whether the sentiment of social shares is positive or negative, and gives an aggregate sentiment rating for the news story.

Enginuity is an awesome tool for finding stories to share through your social channels, as well as getting a combined picture of sentiment about recent events trending on social media.

Revealed Context (API/Excel Add-in): Revealed Context, another popular tool for sentiment analytics on Twitter data, offers a free API for running sentiment analytics on up to 250 documents per day. There’s an Excel add-in as well as a web interface for running analytics independently of the API.

While Revealed Context does not offer an interface for directly scraping Twitter, it can, however, analyze a spreadsheet of tweets without using the API. With the API, you can build a pipeline that feeds recent tweets from the Twitter API into the Revealed Context API for processing.

Steamcrab: Steamcrab is a well-known web application for sentiment analytics on Twitter data. It focuses on keyword searches and analyzes tweets according to a two-pole scale (positive and negative). Visualization options are limited to scatter plots and pie charts.

MeaningCloud (API/Excel Add-in): MeaningCloud is another free API for twitter text analytics, including sentiment analytics. One of the principal advantages of MeaningCloud is that the API supports a number of text analytics operations in addition to sentiment classification. These operations include topic extraction, text classification, part-of-speech tagging etc.

SocialMention (Web App): Socialmention is a basic, search engine-style web app for topic-level sentiment analysis on Twitter data. You can enter a keyword, and the tool will return aggregate sentiment scores for the keyword as well as related keywords.

Another attractive feature of SocialMention is its support for basic brand management use case. It returns a “passion” score that measures how likely Twitter users are to discuss your brand, as well as the average reach of the Twitter users discussing your brand.

Twitter Sentiment Analysis Python Tutorial

You may enroll for a Twitter sentiment analysis python course to understand theory underlying sentiment analysis, and its relation to binary classification, design and Implement a sentiment analysis measurement system in Python, and also identify use-cases for sentiment analysis.

If you enroll for a Twitter Sentiment Analysis Tutorial, you will learn:

  • the different approaches to Twitter Sentiment Analysis: Rule-based and ML-based
  • Sentiment Lexicons to learn about the provide us with lists of words in different sentiment categories that we can use for building our feature set.
  • Regular Expressions
  • Twitter API
  • Sentiment Analysis of Tweets with Python
  • Generate a list of all users who are tweeting about a particular topic.

Twitter Sentiment Analysis Tutorial is well suited for Analytics professionals, modellers, Big Data professionals looking forward to a career in machine learning. Tech executives, product managers, and engineers can also enroll for Twitter Sentiment Analysis Tutorial for big data, machine learning or natural language processing.

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Mastering Python for Twitter Sentiment Analysis or otherwise will prepare you better for a rewarding career in Python. Tremendous growth, enormous learning, and lucrative salary are just some of the well-known perks of a promising career in Python. Add to that the magic touch of a Data Analytics course, and you will soon be counted among the shining glitterati of data scientists.

Digital Vidya offers one of the best-known Data Science courses for a promising career in Data Science using Python. Its industry-relevant curriculum, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya. In addition, students also get lifetime access to online course matter, 24×7 faculty support, expert advice from industry stalwarts, and assured placement support that prepares them better for the vastly expanding Data Science market.

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