Case study twitter feed customer sentiment analysis Data Curry

Extract user sentiments from Twitter data – Implementation Case Study

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CASE STUDYMeasure the performance of a product using unstructured text from Twitter

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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

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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.

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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

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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

Thank You

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