Yoga-Veganism: Correlation Mining of Twitter Health Data
Table 2. The Dominant & 2nd Dominant Topic of a Tweet and corresponding Topic Contribution on that specific Tweet.
['[BOLD] Dataset Train', '[BOLD] Tweets Revoking my vegetarian status till further notice. There’s something I wanna do and I can’t afford the supplements that come with being veggie.', '[BOLD] Dominant Topic 2', '[BOLD] Contribution (%) 61', '[BOLD] 2 [ITALIC] nd Dominant Topic 1', '[BOLD] Contribution (%) 18']
After doing topic modeling using three different method LSA, NMF, and LDA, we use LDA for further analysis i.e. to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet of training data. To observe the model behavior on new tweets those are not included in training set, we follow the same procedure to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet on testing data. Topic 1 is 2nd dominant topic and 18% contribution here. We found Topic 2 as a dominant topic with 33% of contribution and Topic 4 as 2nd dominant topic with 32% contribution in this tweet. Topic 3 is 2nd dominant with 23% contribution which is misleading topic. The model misinterprets the words ‘water in hand’ and infers topic which has keywords ”swimming, swim, pool”. But the model should infer more reasonable topic (Topic 1 which has keywords ”diet, workout”) here. We got Topic 2 as dominant topic for the tweet in 4th row We think during accuracy comparison with ground truth 2nd dominant topic might be considered.