Step by Step: Twitter Sentiment Analysis in Python

Twitter is a platform through which businesses can interact with their customers because Twitter provides “Freedom of Speech” to its users. Here users can express their emotions, opinions, ideas, etc. Using these data, businesses can work on their products. However, there’s so much data available on Twitter that can be analyzed to bring out the sentiments of people.

That’s why Sentiment Analysis turns out to be the game changer for companies, and it is one of the most innovative marketing strategies out there. Sentiment analysis is a tool that can monitor the emotions, perceptions, or criticism from the conversation on social media. This article will guide you on how to use the Sentiment Analysis tool to listen to your Twitter customers and create it using Python.

So, let’s get started.

Contents

What is Sentiment Analysis?

Sentiment analysis is an automated tool used to monitor the subjective information from the raw text; this information can be further analyzed to bring out users’ emotions. The most common sentimental analysis is done by the Polar Detection method. The polar detection method is a method in which we classify the sentiments of the data into three categories: Positive, Negative, and Neutral.

The sentiment analysis tool uses Machine Learning (ML) to give relevant and accurate results and NLP (Natural Language Processing) to make sense of natural language.

You can connect a sentiment analysis tool to your Twitter using python to monitor the tweets 24/7 and get the latest insights from the tweets.

What are the advantages of Sentiment Analysis?

Here is a list of the significant advantages of the Sentiment Analysis tool:

Social Media Monitoring

Using this tool, we can monitor active customer feedback and responses on social media.

Custom Customer Service

Companies can provide custom help support to some new customers who are facing some issues with the products.

Moreover, customer service agents can prioritize the customers according to the issues.

Brand Monitoring

This tool can genuinely help in Brand Monitoring. Whenever a company wants to publish a new design, product, or service, it may want the reactions of its customers.

Market Research

Companies can do market research more efficiently. Twitter is a platform where users tweet about their experience on anything. So these tweets act as the major source of customer insights.

Let’s see the Python tutorial on creating our Sentimental Analysis tool.

Sentiment Analysis of Tweets using Python

There are a few steps you have to do before moving forward:-

Create a developer account on www.developer.twitter.com
Go to igneous->apps and then create a new application]
Then check the details and copy the essential keys from the “Keys and Tokens” option
Copy “Consumer API Keys” and “Access Token and Access token Secret keys”.

Okay, now you are ready to do sentiment analysis. Open your compiler; we use Google Colab because it is an open-source compiler with pre-installed libraries. You can use Jupyter, also.

We have to use the “tweepy” package for accessing Twitter APIs and “TextBlob from textblob” to get the tweets’ text.

CODE:

import tweepy

from textblob import TextBlob

# You have to paste your unique keys here

consumer_key = “uaokaxCIAvgtF7jpuoAiThnkIB”

consumer_key_secret = “sIBrjgY3V1YW7wdTaXzNF75rAi1QXQHW6HUsbkshi4nZ”

access_token = “121345dnjnfoHSIBDnskbUBb8sIBsjww”

access_token_secret = “enjsBbjsbABibsf55sjo6sonwCS66Azs”

auth = tweepy.OAuthHandler(consumer_key, consumer_key_secret)

auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth)

# and we are done with the authentication process, and we will start our work from here

public_tweets = api.search(‘Avengers’)

# using for loop to traverse the tweets

for tweet in public_tweets:

#printing the text of original tweet

print(tweet.text)

# Analysing the text of tweet

analysis = TextBlob(tweet.text)

print(analysis.sentiment)

#using polarity detection for determining the results

if analysis.sentiment[0] > 0:

print(‘Positive’)

elif analysis.sentiment[0] < 0:

print(‘Negative’)

else:

print(‘Neutral’)

#end

..And it’s done; we are ready to execute this. After executing this program, we will get to see the desired results. If the polarity comes out to be 0, the tweet’s sentiment is Neutral. Otherwise, if the polarity comes out to be greater than 0, that means sentiments are positive; negative sentiments are there in the tweet.

Let’s wrap up this discussion here. Hopefully, this article helps you.

See Also: How To Schedule Twitter Threads?

References

Sophia Bernazzani