Sentimental Analysis

Sentiment analysis, also known as opinion mining, is a natural language processing technique used to determine the sentiment or emotional tone expressed in a piece of text. It involves analyzing text to identify whether it expresses positive, negative, or neutral sentiments. This technique is widely used to understand opinions, sentiments, and emotions expressed in online conversations, reviews, or social media posts, it enables businesses and researchers to gauge public sentiment towards products, services, or topics.

Overview

Sentimental Analysis Template discusses several Sentimental Analysis use cases and comes with several ready-to-use Sentimental Analysis workflows dealing with different use cases, leveraging different NLP Models and LLMs.

Use Cases

Applications of sentiment analysis are broad and include:

1. Customer Feedback and Service: Analyzing customer reviews and feedback on products, services, or experiences to gauge overall satisfaction.
2. Social Media Monitoring: Understanding public sentiment about brands, products, campaigns, or events by analyzing posts, comments, and reactions on social media platforms.
3. Market Research and Analysis: Gauging consumer response to products, services, or advertisements for market research purposes.
4. Political Campaigns and Public Opinion: Analyzing public opinion towards political campaigns, candidates, or issues to strategize communication and policies.
5. Stock Market and Financial Trends: Predicting market trends by analyzing investor sentiment and reactions to financial news or company performance.

Sentimental Analysis using Text-classifier

One approach to perform sentimental analysis is by using a text-classifier model. Text-classifier models are trained on labeled datasets, where each text sample is associated with a sentiment label (e.g., positive, negative, neutral). The model learns patterns and features from the training data to classify new, unseen text into the appropriate sentiment category.

There are multiple sentiment analysis text-classifier models available on Clarifai for different use case:

Tweet sentiment Analysis

Sentiment analysis focused on analyzing the sentiment of tweets on Twitter. These tweets often reflect public opinion, emotions, and reactions to various topics, events, products, and services, making them a valuable source of real-time sentiment data.

This technique analyzes the text of tweets to determine whether they are positive, negative, or neutral, helping to gauge public opinion, monitor brand reputation, and understand user responses to events or topics discussed on Twitter.

Tweet sentimental analysis using Clarifai

Tweet sentiment Analysis Workflow: This workflow uses twitter-roberta-base-sentiment-latest model trained on ~124M tweets, and finetuned for sentiment analysis with the TweetEval benchmark. 

Multilingual Sentiment Analysis

Multilingual sentiment analysis extends the basic concept of sentiment analysis across different languages. It involves the process of detecting, extracting, and analyzing sentiments from texts written in multiple languages. This is particularly challenging due to linguistic nuances, cultural differences, idioms, and expressions unique to each language that can significantly affect the sentiment conveyed.

Multilingual sentimental analysis using Clarifai

• Multilingual Sentiment Analysis-DistilBERT Workflow:  This workflow uses distilbert-base-multilingual-cased-sentiments model, finetuned for sentiment analysis in multiple languages.
• Multilingual Sentiment Analysis-BERT Workflow: This workflow uses bert-base-multilingual-uncased-sentiment model, finetuned for sentiment analysis in multiple languages.

Financial sentiments Analysis

Financial sentiment analysis involves identifying and quantifying the emotional tone or sentiment conveyed in financial texts, such as news articles, reports, social media posts, and earnings calls. It aims to discern whether the sentiment is positive, negative, or neutral towards a specific financial entity, event, or market trend, helping investors and analysts gauge market sentiment and make informed decisions.

Financial sentimental analysis using Clarifai

• Financial sentiments Analysis-DistilRoBERTa Workflow: This workflow uses distilroberta-financial-news-sentiment-v2 model, a fine-tuned version of the DistilRoBERTa model on the financial_phrasebank dataset for sentiment analysis of financial news.
• Financial sentiments Analysis-FinBERT Workflow: This workflow uses finbert, A BERT-based model fine-tuned on financial text for high-accuracy sentiment analysis in the finance domain.

Sentimental Analysis using LLMs

Large Language Models (LLMs), like GPT (Generative Pretrained Transformer) variants, since they are pre-trained on diverse internet text, enabling them to understand context, nuance, and the subtleties of human language, making them particularly adept at detecting sentiments within text, ranging from basic positive/negative judgments to more complex emotional analyses.

How It Works

LLMs perform sentiment analysis by processing text inputs and applying their learned understanding of language nuances to classify the sentiment. This process involves understanding the context, detecting sarcasm or irony when present, and distinguishing between positive, neutral, and negative sentiments. These models can be fine-tuned or directly queried using specific prompts to perform sentiment analysis across various domains, including customer feedback, social media monitoring, market research, and more.

sentimental analysis using Clarifai

There are multiple workflows exists with different LLMs optimised with prompt for sentimental analysis. 

sentimental-analysis-mistral-7b Workflow: Workflow uses Mistral-7b Model with specified prompt template for sentimental analysis and predicts if the sentence/ pargraph is positive/negative or neural sentiment.

sentimental-analysis-dbrx: Workflow uses DBRX-Instruct Model with specified prompt template for sentimental analysis and Provide a sentiment score from -1 (most negative) to 1 (most positive) and predicts if the input text is positive/negative or neural sentiment.

LLMs have shown impressive results in various NLP tasks, including sentimental analysis, due to their ability to capture complex linguistic patterns and contextual information.

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