SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA FOR BRAND PERCEPTION ANALYSIS.

SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA FOR BRAND PERCEPTION ANALYSIS.

Abstract:
In the digital age, social media platforms have become pivotal in shaping brand perception and consumer sentiment. Understanding how users perceive and discuss brands on social media is crucial for businesses to make informed decisions regarding their marketing strategies and overall brand management. Sentiment analysis, a subfield of natural language processing, offers a valuable approach to extract and analyze sentiment from social media data.

This abstract provides an overview of the research conducted on sentiment analysis of social media data for brand perception analysis. The study aims to explore the effectiveness of sentiment analysis techniques in capturing and evaluating sentiment towards brands on various social media platforms.

The research methodology involves collecting a large dataset of social media posts and comments related to different brands across multiple platforms such as Twitter, Facebook, and Instagram. The collected data is preprocessed to remove noise, perform text normalization, and extract relevant features for sentiment analysis. Various sentiment analysis algorithms, including lexicon-based approaches, machine learning techniques, and deep learning models, are applied to classify the sentiment expressed in the social media data.

The results of the sentiment analysis are then analyzed to gain insights into brand perception. The study focuses on identifying positive, negative, and neutral sentiments associated with specific brands, as well as detecting sentiment shifts over time. The findings aim to provide valuable information for brand managers to assess the strengths and weaknesses of their brand reputation and make data-driven decisions to improve brand perception.

Furthermore, the research explores the correlation between sentiment expressed on social media and other brand-related metrics, such as sales, customer satisfaction, and brand loyalty. By understanding this relationship, businesses can better gauge the impact of social media sentiment on their overall brand performance and devise strategies to leverage positive sentiment and mitigate negative sentiment.

In conclusion, sentiment analysis of social media data offers a powerful tool for brand perception analysis. By leveraging advanced natural language processing techniques, businesses can gain valuable insights into consumer sentiment, identify emerging trends, and make informed decisions to enhance their brand's reputation and overall success in the marketplace.

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