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Case study twitter feed customer sentiment analysis Data Curry
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Transcript of Case study twitter feed customer sentiment analysis Data Curry
![Page 1: Case study twitter feed customer sentiment analysis Data Curry](https://reader033.fdocuments.net/reader033/viewer/2022061223/54c6bc6a4a795943608b4578/html5/thumbnails/1.jpg)
Extract user sentiments from Twitter data – Implementation Case Study
![Page 2: Case study twitter feed customer sentiment analysis Data Curry](https://reader033.fdocuments.net/reader033/viewer/2022061223/54c6bc6a4a795943608b4578/html5/thumbnails/2.jpg)
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CASE STUDYMeasure the performance of a product using unstructured text from Twitter
![Page 3: Case study twitter feed customer sentiment analysis Data Curry](https://reader033.fdocuments.net/reader033/viewer/2022061223/54c6bc6a4a795943608b4578/html5/thumbnails/3.jpg)
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The problem statement
Twitter being one of the most active and real time social networking tool, it swiftly reflects the mood of users of a product or service.
Here, we measure the performance of a newly released laptop using the free-form text from Twitter
Obtaining user emotions for specific features of laptop rather than the laptop as a whole
Take preventive actions in order to provide proactive customer services and thereby improve the product sales by end first Quarter
![Page 4: Case study twitter feed customer sentiment analysis Data Curry](https://reader033.fdocuments.net/reader033/viewer/2022061223/54c6bc6a4a795943608b4578/html5/thumbnails/4.jpg)
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Challenges
Huge volumes of free-form data
Processing the tweets: twitter lingo constantly evolves, new names and characterizations flare up all the time, which excludes straightforward full-text analysis.
Users express their emotions on diverse issues related to a particular feature or several features of the product
Classify based on user emotions as positive, negative or neutral.
![Page 5: Case study twitter feed customer sentiment analysis Data Curry](https://reader033.fdocuments.net/reader033/viewer/2022061223/54c6bc6a4a795943608b4578/html5/thumbnails/5.jpg)
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Solution approach/methodology
Extract Tweets
Pre-process
Algorithmic scoring of
tweets
Feature based sentiment extraction
Visualize the results
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Results and analysis
Extracted the Key features of the product
Rating of the importance of each feature
Sentiment score on the feature is analyzed
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Key features extracted
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Sentiment by key features
Camera Battery RAM Memory Reader Digital Media Bluetooth Speed0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Neutral
Negative
Positive
Key features of the laptop
% se
ntim
ent f
rom
the
data
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Summary, conclusions and/or benefits
Pin-pointed the potential causes of negative sentiments
Identified the broader trends on the perception
Direct engagement rate with customers increased
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Thank You
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