Abstract
Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a large amount of social media text is characterized by the presence of Code-Switching. Thus, it has become important to build models that can handle code-switched data. However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms. We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data. We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer. We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question - ‘Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?’. Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7% (weighted F1 scores) when compared to supervised models trained for a two class problem.
Highlights
Sentiment analysis, sometimes known as opinion mining, aims to understand and classify the opinion, attitude and emotions of a user based on a text query
We present a general framework called Unsupervised Self-Training Algorithm for doing sentiment analysis of code-mixed data in an unsupervised manner
We present a rigorous analysis of the learning dynamics of our unsupervised model and try to answer the question - ’Does the unsupervised model understand the code-switched languages or does it just recognize its representations?’
Summary
Sometimes known as opinion mining, aims to understand and classify the opinion, attitude and emotions of a user based on a text query. Code switching is very common in many bilingual and multilingual societies around the world including India (Hinglish, Tanglish etc.), Singapore (Chinglish) and various Spanish speaking areas of North America (Spanglish). A large amount of social media text in these regions is code-mixed, which is why it is essential to build systems that are able to handle code switching. Various datasets have been released to aid advancements in Sentiment Analysis of code-mixed data. These datasets are usually much smaller and more noisy when compared to their high-resourcelanguage-counterparts and are available for very few languages. We present results for four code-mixed languages - Hinglish (Hindi-English), Spanglish (Spanish-English), Tanglish (Tamil-English) and Malayalam-English
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call