twitter sentiment analysis

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  • The University of Texas at Dallas utdallas.edu

    Airline Twitter Analysis

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  • The University of Texas at Dallas utdallas.edu

    What we wanted to do?

    Kaggle- Twitter Airlines Sentiments Exploratory Analysisi. When do people tweet?ii. Which airlines gets the most tweets?iii. Which sentiments are dominant?iv. How these sentiments are distributed? Text Analyticsi. Most frequently used wordsii. Most frequently used words when the sentiment is negative.iii. Most frequently used words when the sentiment is positive.iv. Tweet length vs Sentiment

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  • The University of Texas at Dallas utdallas.edu

    Cleansing of data

    Tweets Had @airline name at the beginning of every tweet

    4 columns with hardly any data

    Null and missing values

    Co-Ordinates required - Geo coding

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  • The University of Texas at Dallas utdallas.edu

    When do people tweet?

    Most of the tweets have come in during the rush morning hours peaking at 9 am

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    Number of Tweets every hour

  • The University of Texas at Dallas utdallas.edu

    How are the tweets & sentiments distributed?

    United Airlines, American and US Airways receive most of the tweets.

    Most of the tweets are negative as expected.

    63% of the tweets are negative.

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  • The University of Texas at Dallas utdallas.edu

    Distribution of sentiments for all the airlines

    Sentiment frequency

    Positive 0.1706621Neutral 0.2295947Negative 0.5997432

    The three airlines having maximum tweets are the ones having maximum negative tweets? Why?

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  • The University of Texas at Dallas utdallas.edu

    Why so many negative tweets?

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  • The University of Texas at Dallas utdallas.edu

    Word clouds to show frequency of words used in negative tweets

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    US Airways United Airlines American Airlines

  • The University of Texas at Dallas utdallas.edu

    An outlier in the case of Delta Airlines

    .

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  • The University of Texas at Dallas utdallas.edu

    Word cloud for all the positive tweets

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  • The University of Texas at Dallas utdallas.edu

    From which time zones are people tweeting ?

    Flights travel everywhere throughout the world.

    But we observed that most of the tweets originate from the Eastern Time zone(US & Canada).

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  • The University of Texas at Dallas utdallas.edu

    Association Analysis

    Association Analysis on words used in the tweet.

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  • The University of Texas at Dallas utdallas.edu

    Hierarchical clustering to determine association between words

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  • The University of Texas at Dallas utdallas.edu

    Contd

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  • The University of Texas at Dallas utdallas.edu

    Kmean clustering

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  • The University of Texas at Dallas utdallas.edu

    Contd

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  • The University of Texas at Dallas utdallas.edu

    Association between Tweet length and sentiment

    Longer the tweet, we observed they are likely to be negative in sentiment.

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  • The University of Texas at Dallas utdallas.edu

    Contd

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  • The University of Texas at Dallas utdallas.edu

    What else we tried doing?

    A predictive model

    Setbacks we faced during the process

    Work on SPSS

    Categorization

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  • The University of Texas at Dallas utdallas.edu

    Why this Analysis? Will it help in some way?

    Airline Industry lives on customers.

    We get to know where we are doing good and where we are doing bad.

    Can be a basis for a predictive model when we associated tweet length with sentiment.

    Companies can get to know their competition.

    Improve the flight journey overall.

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  • The University of Texas at Dallas utdallas.edu

    References

    Wikipedia.com

    Kaggle.com

    www.clarabridge.com/text-analytics/

    https://sites.google.com/site/manabusakamoto/home/r.../r-tutorial-3

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    http://www.clarabridge.com/text-analytics/