Sentiment Analysis

959 papers with code • 40 benchmarks • 74 datasets

Sentiment analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

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Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert NAACL 2019

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Convolutional Neural Networks for Sentence Classification

facebookresearch/pytext EMNLP 2014

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.

Universal Language Model Fine-tuning for Text Classification

fastai/fastai ACL 2018

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.

Bag of Tricks for Efficient Text Classification

facebookresearch/fastText EACL 2017

This paper explores a simple and efficient baseline for text classification.

A Structured Self-attentive Sentence Embedding

jadore801120/attention-is-all-you-need-pytorch 9 Mar 2017

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.

RoBERTa: A Robustly Optimized BERT Pretraining Approach

pytorch/fairseq 26 Jul 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

Deep contextualized word representations

flairNLP/flair NAACL 2018

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

google-research/bert ICLR 2020

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.

Domain-Adversarial Training of Neural Networks

PaddlePaddle/PaddleSpeech 28 May 2015

Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.

Character-level Convolutional Networks for Text Classification

gaussic/text-classification-cnn-rnn NeurIPS 2015

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.