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A classifier written in Python to classify the tweets pertaining to Obama and Romney into positive, negative, neutral or mixed sentiments to predict the outcome of the 2012 Presidential Elections

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Sentiment-Analysis-of-tweets

To analyze the sentiments of people by analyzing their tweets about the presidential candidates and classify them into positive, negative or neutral sentiments. The tweets here refer to the ones from the 2012 Presidential elections between the two candidates – Barrack Obama and Mitt Romney. The result of classification will give us a possible outcome of the election.

Test Data Description: The given test data was in an Excel spreadsheet by the name –‘Project2_Testing.xlsx’. It consisted about 600 tweets with their respective class labels. The respective tweets were in separate sheets under the names –‘Obama-test’ and ‘Romney-test’. The experimental results are as follows:

RESULTS FOR OBAMA:

Overall Accuracy: 54.6%

                            Positive   Negative   Neutral

                 Precision   0.7333     0.6667     0.3402

                 Recall      0.2374     0.6905     0.6055

                 F-Score     0.3587     0.6784     0.4356

Confusion Matrix:

                            Positive   Negative   Neutral

                 Positive     33         48         58

                 Negative      8        174         70 

                 Neutral       4         39         66 

RESULTS FOR ROMNEY:

Overall Accuracy: 56.6%

                            Positive   Negative   Neutral

                 Precision    0.6       0.7791    0.2688

                 Recall     0.3391      0.6643    0.5376

                 F-Score    0.4333      0.7172    0.3584

Confusion Matrix:

                            Positive   Negative   Neutral

                 Positive      39        29         47

                 Negative       9        194        89

                 Neutral       17         26        50

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A classifier written in Python to classify the tweets pertaining to Obama and Romney into positive, negative, neutral or mixed sentiments to predict the outcome of the 2012 Presidential Elections

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