Getting Started with Sentiment Analysis using Python

Getting Started with Sentiment Analysis on Twitter

How to Fine-Tune an SLM for Emotion Recognition

Python tutorial for fine-tuning a Mistral Small 3. 1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .

MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

arXiv:2309.15670v3 Announce Type: replace Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh m...

Unsupervised sentiment neuron

We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.

SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis

arXiv:2607. 05259v1 Announce Type: cross Abstract: Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications.

Wazobia Eval: A Benchmark for Nigerian Pidgin Emotion Understanding, Sarcasm Detection, and Cultural Reasoning

arXiv:2608.21369v1 Announce Type: cross Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchma...

LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks

This paper presents an empirical comparison of lexicon-based and Large Language Model (LLM)-based sentiment analysis for extracting market-relevant signals from social media discourse in highly volatile equity markets. Using Reddit data from r/WallStreetBets and focusing on meme stocks (GME, AMC, NOK), we construct time-aligned sentiment indicators and evaluate their relationship with market returns, with particular attention to extreme positive return events in the upper tail of the return distribution.

Quantifying Affective Bias in Low-Resource Media: Large-Scale Emotion Profiling of Bengali Headlines

The paper investigates affective framing in Bengali digital journalism by analyzing 300,000 news headlines with zero‑shot inference using Gemma 3 4B to estimate dominant emotions and overall affective tone. Results reveal frequent negative emotions—especially anger, sadness, disappointment, and fear—across the corpus, and a pilot validation on 200 headlines indicates the model’s estimates are useful yet not definitive benchmarks. The authors suggest a bias‑sensitive news interface that visualizes emotional cues to help readers detect affective framing patterns in daily news.

Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation

arXiv:2608. 05155v1 Announce Type: cross Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH).

Multiclass Sentiment Analysis for Identifying Political Viewpoints

arXiv:2608. 11049v1 Announce Type: cross Abstract: The rapid growth of social media has created vast amounts of political discourse, which provides valuable opportunities to analyze public opinions and identify different political perspectives.

Fault of Our Stars: Behavioral Drivers of Rating-Sentiment Incongruence

When people share experiences online, they often express thoughts in two ways: a star rating and a written review. In sentiment analysis, ratings are widely used as convenient weak labels for textual sentiment, yet whether the two actually agree is rarely questioned.

Sentiment Analysis on Encrypted Data with Homomorphic Encryption