  Sentiment analysis (SA) has become an extensive research area in recent years
impacting diverse fields including ecommerce, consumer business, and politics,
driven by increasing adoption and usage of social media platforms. It is
challenging to extract topics and sentiments from unsupervised short texts
emerging in such contexts, as they may contain figurative words, strident data,
and co-existence of many possible meanings for a single word or phrase, all
contributing to obtaining incorrect topics. Most prior research is based on a
specific theme/rhetoric/focused-content on a clean dataset. In the work
reported here, the effectiveness of BERT(Bidirectional Encoder Representations
from Transformers) in sentiment classification tasks from a raw live dataset
taken from a popular microblogging platform is demonstrated. A novel T-BERT
framework is proposed to show the enhanced performance obtainable by combining
latent topics with contextual BERT embeddings. Numerical experiments were
conducted on an ensemble with about 42000 datasets using NimbleBox.ai platform
with a hardware configuration consisting of Nvidia Tesla K80(CUDA), 4 core CPU,
15GB RAM running on an isolated Google Cloud Platform instance. The empirical
results show that the model improves in performance while adding topics to BERT
and an accuracy rate of 90.81% on sentiment classification using BERT with the
proposed approach.
