Twitter sentiment analysis is a powerful tool that can help brands, influencers, and celebrities in marketing and leveraging their products/brands to get an image makeover. In this response, we will discuss how Twitter sentiment analysis can help them achieve their marketing goals.

• Twitter sentiment analysis can help brands, influencers, and celebrities to understand their target audience better. This information can help them create targeted marketing campaigns and content that resonates with their audience, resulting in better engagement and conversions.

• Help brands, influencers, and celebrities to measure their brand perception accurately. By analyzing the sentiment of tweets they can track the sentiment over time and make data-driven decisions to improve their brand image.

• By analyzing the sentiment of tweets, they can identify the top influencers who have a positive sentiment towards their brand. They can then collaborate with these influencers to create content that promotes their brand and products to a wider audience, resulting in higher brand awareness and sales.

• Helps brands, influencers, and celebrities to manage crises effectively. By monitoring the sentiment of tweets during a crisis, they can understand the severity of the situation and take appropriate action to address it.

• Analyzing the sentiment of tweets that mention their competitors, they can understand how their brand compares to their competitors. They can then identify their strengths and weaknesses and take corrective action to improve their brand image.

In conclusion, Twitter sentiment analysis is a powerful tool that can help brands, influencers, and celebrities in marketing and leveraging their products/brands to get an image makeover. By understanding their target audience, measuring brand perception, identifying influencers, crisis management, and competitor analysis, they can create data-driven marketing campaigns and content that resonates with their audience, resulting in better engagement and conversions.

Fig 1 - System-Flow

The user interface for it is provided via a web app with key instructions and features. Tweets could be fetched within the range 1 to 100, these tweets can be ordered via two methods by hashtag/text-search

Fig 2 - Landing page

The tweets after being retrieved in the backend in the csv/tabular form is fed into the corresponding fields in the frontend thereby producing a powerful visualization dashboard

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Fig 3 - Username profile metrics and sentiment analysis

The dashboard can be divided into different sections for easy understanding; the first section shows the user’s profile metrics like the count of the user’s friends, followers, number of tweets and number of tweets liked by the user the second section includes classifying the sentiments of the tweets into three categories namely – positive, negative and neutral in the form of a pie chart (the model training/testing details can be found here)

Installation

1. Fork the Project
2. Clone your forked repository on your host machine

  git clone https://github.com/SayanSaha01/TrendScout.git
  cd TrendScout

3. To install the dependencies and packages on your host machine

pip install -r requirements.txt

  py -m streamlit run TrendScout.py