Twitter Trends Analysis Using Python

Twitter Trends Analysis Using Python engages in gathering, processing and evaluating more prevalent topics. For detecting track sentiment, trending discussions and anticipating trends, this analysis is more applicable. To carry out Twitter trends analysis with the application of Python, we offer a detailed guide with simple steps:

Step 1: Install the Required Libraries

For conducting analysis, processing data and communicating with Twitter’s API, we may require multiple Python libraries.

pip install tweepy pandas matplotlib wordcloud textblob

• Tweepy: Deploy Tweepy for approaching the Twitter API.
• Pandas: We can use Pandas for data manipulation.
• Matplotlib: Plot graphs and visualizations by using Matplotlib.
• WordCloud: Wordcloud visualizations are developed with the aid of WordCloud.
• TextBlob: Utilize the TextBlob for performing sentiment analysis.

Step 2: Configure the Twitter API Access

Then, design a Twitter Developer account and acquire our API keys to enable the APT of Twitter:

1. First, click the Twitter Developer Portal.
2. To obtain our API keys such as Access token Secret, API secret key, API key and Access token, design an innovative app.
3. In a protective manner, save these documents.

Step 3: Access Twitter Trends Using Tweepy

In order to acquire highly prevalent topics on Twitter, a detailed program is provided here:

import tweepy

# Replace these values with your own credentials

API_KEY = ‘your_api_key’

API_SECRET = ‘your_api_secret’

ACCESS_TOKEN = ‘your_access_token’

ACCESS_TOKEN_SECRET = ‘your_access_token_secret’

# Authenticate with Twitter

auth = tweepy.OAuthHandler(API_KEY, API_SECRET)

auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

api = tweepy.API(auth, wait_on_rate_limit=True)

# Get trending topics for a specific location (WOEID)

# 1 for global trends, 23424977 for the USA, or use WOEID for other regions

trends_result = api.get_place_trends(id=1)  # Global trends

# Display trending topics

for trend in trends_result[0][‘trends’]:

print(trend[‘name’], trend[‘tweet_volume’])

Step 4: Evaluate and Visualize Trends

It is required to carry out different analyses like evaluating the sentiment of tweets on the basis of trends or detecting the majorly addressed subjects, after we obtain the widely recognized topics.

Example 1: Visualizing Trending Topics

To exhibit the significant prevalent trends, a bar chart can be designed:

import pandas as pd

import matplotlib.pyplot as plt

# Extract trends and their tweet volumes

trends = [trend[‘name’] for trend in trends_result[0][‘trends’]]

tweet_volumes = [trend[‘tweet_volume’] for trend in trends_result[0][‘trends’]]

# Create a DataFrame

df_trends = pd.DataFrame({‘Trend’: trends, ‘Tweet Volume’: tweet_volumes})

# Clean data (remove None values)

df_trends = df_trends.dropna()

# Sort by tweet volume

df_trends = df_trends.sort_values(‘Tweet Volume’, ascending=False)

# Plot the data

plt.figure(figsize=(10, 8))

plt.barh(df_trends[‘Trend’], df_trends[‘Tweet Volume’], color=’skyblue’)

plt.xlabel(‘Tweet Volume’)

plt.title(‘Top Twitter Trends’)

plt.gca().invert_yaxis()

plt.show()

Example 2: Sentiment Analysis of a Trending Topic

In accordance with a popular topic, we must conduct sentiment analysis on tweets by utilizing TextBlob.

from textblob import TextBlob

# Choose a trending topic

trending_topic = df_trends[‘Trend’].iloc[0]

# Search for tweets related to the trending topic

tweets = tweepy.Cursor(api.search_tweets, q=trending_topic, lang=”en”).items(100)

# Analyze sentiment

sentiments = []

for tweet in tweets:

analysis = TextBlob(tweet.text)

sentiment = analysis.sentiment.polarity

sentiments.append(sentiment)

# Visualize sentiment

plt.hist(sentiments, bins=10, color=’skyblue’)

plt.xlabel(‘Sentiment Polarity’)

plt.ylabel(‘Tweet Count’)

plt.title(f’Sentiment Analysis of Tweets about {trending_topic}’)

plt.show()

Example 3: Word Cloud of Trending Topics

Regarding a specific pattern, it is required to exhibit the more general words in the tweets through developing a word cloud:

from wordcloud import WordCloud

# Get tweets related to the top trending topic

tweets = tweepy.Cursor(api.search_tweets, q=trending_topic, lang=”en”).items(200)

# Join all the tweets into a single string

text = ” “.join([tweet.text for tweet in tweets])

# Generate the word cloud

wordcloud = WordCloud(width=800, height=400, background_color=’white’).generate(text)

# Display the word cloud

plt.figure(figsize=(10, 5))

plt.imshow(wordcloud, interpolation=’bilinear’)

plt.axis(‘off’)

plt.title(f’Word Cloud of Tweets about {trending_topic}’)

plt.show()

Step 5: Extend the Analysis

For the purpose of enhancing our analysis of Twitter trends, further ideas are listed below:

1. Trend Evolution over Time: For several days or up to many hours, monitor the prevalent topic on how it progresses efficiently.
2. Hashtag Analysis: Based on widely recognized topics, it is significant to evaluate the consumption of particular hashtags.
3. Geographical Trends: Among various areas, concentrate on contrasting the popular topics.
4. Sentiment Trend Analysis: Eventually, it is crucial to monitor the sentiment on how it modifies across certain topics.
5. Network Analysis: Depending on highly prevalent topics, we have to configure and assess networks of users and hashtags.

Twitter trends analysis using python projects

To assist you in acquiring perspectives on popular topics on twitter, forecasting upcoming trends and interpreting public sentiment, some of the research-worthy projects on twitter trend analysis in Python are recommended by us:

Simple Twitter Trends Analysis Projects

1. Global Trends Visualization: Specifically for visualizing with pie charts or bar charts, major universal trends on Twitter should be gathered and visualized.
2. Country-Specific Trends Analysis: In various places, make use of Twitter’s WOEID (Where on Earth ID) to contrast the significant popular topics.
3. Hashtag Popularity Analysis: Through evaluating the tweet volume, trending of particular hashtags are supposed to be monitored eventually.
4. Trending Topics Sentiment Analysis: To specify the entire sentiment as positive, negative or neutral, we need to conduct sentiment analysis on tweets on the basis of popular subjects.
5. Top Influencers in a Trending Topic: By means of evaluating the tweet volume and retweets, major publicists are intended to be detected by us.
6. Trend Comparison over Time: In the context of sentiment and tweet volume, it is approachable to contrast various widely recognized topics, in what way it develops in the course of time.
7. Word Cloud of Trending Topics: Depending on popular subjects, word clouds are required to be developed for most general words in tweets.
8. Geographical Trends Comparison: Regarding various places or locations, prevalent topics are meant to be contrasted.
9. Trend Evolution Visualization: Across a particular duration, concentrate on exhibiting the popular topic on how it modifies the prevalence among people.
10. Trends Correlation Analysis: Among various prevalent topics, we must observe if it impacts each other through evaluating the relationship.

Middle-Level Twitter Trends Analysis Projects

1. Sentiment Analysis over Time: Periodically, on the basis of a certain highly prevalent subject, we focus on monitoring the sentiment of tweets as a means to examine in what manner it varies accordingly.
2. Hashtag Network Analysis: Considering the more advanced topics, it is required to construct and evaluate the networks of associated hashtags.
3. Top Keywords in Trends: By using NLP (Natural Language Processing), we need to detect the more general keywords related to popular topics.
4. Twitter Bot Detection: Through detecting the patterns in tweet behavior, identify bots which get involved in more prevalent topics by assessing tweets.
5. Retweet Network Analysis: For a popular topic, we must construct a retweet network and the distribution of data is supposed to be evaluated.
6. Topic Modeling on Trending Topics: Among the emerging trends, identify the major significant topics by utilizing topic modeling such as LDA.
7. Hashtag Clustering: From more prevalent topics, concentrate on detecting more general subtopics or concepts.
8. Trend Sentiment Heatmap: For various popular subjects, visualize the sentiment of tweets eventually through modeling a heatmap.
9. Trending Topic Prediction: Depending on past records, we should forecast the trending topic by designing an efficient machine learning framework.
10. Engagement Analysis: As regards widely recognized topics, interpret the user communication through assessing the engagement such as retweets, replies and likes on tweets.

Modern Twitter Trends Analysis Projects

1. Real-Time Trend Monitoring Dashboard: In order to monitor, examine, and visualize twitter trends when they occur, a real-time dashboard needs to be modeled.
2. Influence Analysis of Trending Topics: With the aid of network analysis, critical impacts of particular users on wide distribution of prevalent topics should be assessed.
3. Anomaly Detection in Trends: To detect probable false details or abnormal patterns, outliers have to be recognized in sentiment or tweet volumes.
4. Sentiment Prediction Using Machine Learning: Regarding more prevalent topics, anticipate the sentiment of tweets by training a machine learning framework.
5. Trend Evolution Prediction: It is approachable to anticipate the widely recognized topics in what way it progresses during the next couple of hours by developing an advanced model.
6. Twitter Trend API: Particularly for developers to implement in their applications, an advanced API must be developed that effectively offers trending topic analysis and sentiment data.
7. Comparative Analysis of Twitter and Google Trends: As a means to observe if there’s a connection, we need to contrast Google trends with popular topics on Twitter.
8. Trend Diffusion Model: Through the utilization of epidemiological frameworks such as SIR (Susceptible, Infected, Recovered), the distribution of more prevalent topics is meant to be designed efficiently.
9. Cross-Platform Trend Analysis: On various social media environments such as Reddit or Instagram, we must contrast the Twitter trends with trends.
10. Historical Trend Analysis: To detect the repeated topics or enduring patterns, it is required to evaluate the past records of Twitter trends.

Twitter Trends in Specialized Contexts

1. Political Trends Analysis: In accordance with government schemes, politics and elections, we need to emphasize on evaluating Twitter trends.
2. Corporate Social Responsibility Trends: It is required to evaluate the companies on how their CSR activities are more popular and in what way the people respond to them.
3. Sports Event Trends: At the time of significant sports scenarios like SuperBowl, World Cup and Olympics, we should monitor and evaluate the patterns.
4. Disaster Response Trends: In the course of natural disasters or emergencies, it is approachable to explore the evolution of twitter trends and analyze the reactions of the public regarding it.
5. Entertainment Industry Trends: According to TV shows, celebrities or movies monitor the more prevalent subjects and their implications are meant to be evaluated.
6. Health-Related Trends: Considering the health subjects like public response to epidemic diseases or health programs, evaluate the crucial trends.
7. Financial Market Trends: Examine the trends in an effective manner on how it is associated with stocks or financial markets which advance on Twitter.
8. Social Movements Trends: Based on social activities such as #MeToo or #BlackLivesMatter, our team focuses on monitoring and evaluating Twitter trends.
9. Event-Driven Trends: It is advisable to examine particular scenarios such as news conferences and inaugural functions on how it stimulates the trends on Twitter.
10. Brand Reputation Monitoring: To evaluate the brand impression, twitter trends in accordance with particular brands must be tracked and evaluated.

Twitter Trends and Visualization Projects

1. Interactive Trends Map:  For the purpose of exhibiting prevalent topics in various places, an interactive map must be developed.
2. Sentiment Timeline Visualization: A timeline visualization needs to be designed for exhibiting the sentiment of tweets on how it modifies eventually for a specific trend.
3. Trending Topics Word Cloud Generator: Considering the popular subjects, create word clouds by developing a tool and on a website, exhibit it efficiently.
4. Trend Sentiment Comparison: In a single visualization, the sentiment of various prevalent topics needs to be contrasted simultaneously.
5. Hashtag Sentiment Analysis: Among highly trending topics, sentiment which is connected with particular hashtags ought to be evaluated and the final outcome must be exhibited.
6. Network Graph Visualization: Especially for visualizing the relationship, a network graph of users and hashtags which opened the way for modern and innovative topics need to be developed.
7. Geographical Sentiment Analysis: Over several places, detect the regional variances through map the sentiment of prevalent subjects.
8. Trend Volume Prediction Dashboard: To anticipate and eventually exhibit the tweet capacity of highly prevalent topics through designing a dashboard.
9. Topic Sentiment Overlap: Among the sentiments of various subjects which are popular concurrently, focus on visualizing the convergence.
10. Interactive Trend Analysis Tool: In order to examine user communications, patterns and sentiment on Twitter, an interactive tool should be created by us.

By this article, you can get to know about the significant measures which are involved in performing Twitter trends analysis using Python. Additionally, we propose some extensive research topics that can be suitable for intensive exploration.

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