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Twitter sentiment analysis

Sentiment analysis is also popularly known as “opinion mining”. Sentiment analysis is a procedure of precision about a provided information whether it is of neutral, negative, or positive sentiment on any provided topic. Sentiment analysis also refers to the utilization of natural language processing to understand the emotions, attitudes, and opinions of a writer, speaker or other distinct topics inside the mention of an online network. This process can also be termed as the polarity of the content.

Being humans, subconsciously we can distinguish between a negative or positive test. For instance, “The children have beautiful smiles on their faces”, this sentence is positive to our perceptions. The words “beautiful” and “smiles” are positive while words like “faces”, “children”, and “the” are neutral. Hence, the full sentence offers a positive impact. This process is utilized in social media sites like Twitter to understand the feelings of the users regarding any topic via their “tweets” or textual posts.

Twitter sentiment analysis

What will you require to perform this process?

  • Basic understanding of programming language like python.
  • Python must be installed in your system.
  • Some knowledge of Twitter API. Install Tweepy, as it is the python customer for the official API of Twitter.
  • Understand the distinction between the test data sets and training data sets.

Steps to perform:

  • Preparation of the test sets. After registration and authentication make a function and download the tweets depending on the searched keywords.
  • Preparation of the training set. You can save and retrieve the tweets into the CSV file via API.
  • Pre-processing of the tweets in the data sets. It must be remembered that punctuations, emoji, videos, images, URLs do not have any impact on sentiment analysis. Hence, keep the words and eliminate rest. Keep duplicate words. Install TextBlob to process the textual data.
  • Train the classifier. You can utilize the Naive Bayes Classifier and follow the theorem: P(A|B) = P(B|A)* P(A)/P(B).
  • Now test the model.

Sentiment analysis is an interesting process to understand the application of natural language processing in the creation of the automated conclusion of any texts. This process is chiefly utilized for marketing objectives or to analyze the trends of social media. Hence, sentiment analysis is a method of ‘computationally’ determination of a piece of text whether it being positive or neutral or negative.

Why do we require this process?

  • Politics: to track political views. To prophesize election results and to detect inconsistency and consistency between actions and statements at the government position.
  • Public actions: Analyze and monitor social phenomenon. To understand the emotions of the blogosphere. Or spotting potentially dangerous occurrences.
  • Business: To improve the strategies of marketing. To know about any client’s feelings regarding any services or brands or products. To know about the preferences of customers and to understand the response of any new product.

But Twitter may approve your application for utilization of the Twitter API after a few days of your submission. A minimum of one day is required for approval. Also, downloading the training set above 10 hours will be required.