abhishekmamidi123 / absa-codemix

Research - The aim of this project is to find the aspect from a given code-mix sentence. The traditional sequence tagging methods are compared with Deep learning methods. The concept of Question and Answering model is used to achieve this task.

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