Aspect Based Sentimental Analysis Codemix.

Data:

• We have collected twitter codemix data. After preprocessing the data, we are left with 3500 tweets with Aspects annotated.

Progress:

• We have divided our project into two parts - Aspect Identification and Sentimental Analysis.
• Working on Aspect Identification -- Ongoing work.

Contents:

Parts of speech tagger for codemix:

• Parts of speech plays an important role in Aspect Identication.
• We have 1000 codemix tweets with annoatated parts-of-speech. We used this data for training.
• We have trained the data using different Machine learning methods and achieved the following accuracies:
  • Conditional Random Fields(CRF): 77.185%
  • Trigram + Bigram + Unigram: 68.172%
  • Hidden Markov Method(HMM): 18.014%

Aspect Identification:

• Till now, we applied CRF and SVM methods for identifying Aspects in a sentence.
• CRF: We have used nearly 65 features(considering window length of 5) and achieved an accuracy of 44.73%.
• SVM: We represented each word in the form of a vector and trained these vectors.

Contributors:

M R Abhishek and K Vagdevi