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Awesome GitHub RepositoriesSentiment Analysis Tools

Software for classifying the emotional tone of text, audio, or other content as positive, negative, or neutral.

Distinct from Sentiment Classifiers: Distinct from Sentiment Classifiers (which are models/architectures) and Market Sentiment Analyzers (which are domain-specific); this covers general-purpose sentiment labeling tools.

Explore 45 awesome GitHub repositories matching artificial intelligence & ml · Sentiment Analysis Tools. Refine with filters or upvote what's useful.

Awesome Sentiment Analysis Tools GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • hankcs/hanlphankcs 的头像

    hankcs/HanLP

    36,413在 GitHub 上查看↗

    HanLP is a natural language processing library and deep learning framework specifically optimized for the Chinese language, while also functioning as a multilingual text processor. It serves as a toolkit for performing linguistic analysis, semantic understanding, and script conversion. The project distinguishes itself through a dedicated focus on Chinese linguistic structures, including a specialized script converter for transforming text between Simplified Chinese, Traditional Chinese, and Pinyin. It further supports domain-specific model training to improve the recognition of professional t

    Classifies the emotional tone of text as positive, negative, or neutral.

    Pythondependency-parserhanlpnamed-entity-recognition
    在 GitHub 上查看↗36,413
  • d2l-ai/d2l-end2l-ai 的头像

    d2l-ai/d2l-en

    29,001在 GitHub 上查看↗

    This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

    Provides tools for applying trained models to classify the sentiment of new text inputs.

    Pythonbookcomputer-visiondata-science
    在 GitHub 上查看↗29,001
  • yikart/aitoearnyikart 的头像

    yikart/AiToEarn

    21,287在 GitHub 上查看↗

    AiToEarn is an artificial intelligence-driven social media management platform designed to centralize content orchestration, audience engagement, and performance analytics. It provides a unified workspace where users can generate, optimize, and schedule content across multiple social networks while automating interactions through intelligent language and media models. The platform distinguishes itself by integrating sentiment analysis to categorize audience engagement and identify purchase intent, allowing for personalized automated responses. It utilizes a credit-based accounting system to m

    Processes incoming messages and comments using sentiment analysis to categorize engagement and provide personalized responses.

    TypeScriptauto-publishdouyindouyin-api
    在 GitHub 上查看↗21,287
  • ai4finance-foundation/fingptAI4Finance-Foundation 的头像

    AI4Finance-Foundation/FinGPT

    20,507在 GitHub 上查看↗

    FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial domain. It provides a set of pipelines for financial entity extraction, sentiment analysis, and retrieval-augmented generation to improve the accuracy of financial information systems. The project distinguishes itself through efficient training workflows, utilizing low-rank adaptation and quantized low-rank adaptation to fine-tune models on consumer-grade hardware. It employs market-labeled datasets and reinforcement learning that uses actual stock price movements as reward sig

    Provides a system for extracting sentiment from news and social media to generate trading signals and price predictions.

    Jupyter Notebookchatgptfinancefingpt
    在 GitHub 上查看↗20,507
  • anthropics/claude-quickstartsanthropics 的头像

    anthropics/claude-quickstarts

    17,085在 GitHub 上查看↗

    Claude Quickstarts is a development framework and collection of reference implementations designed for building autonomous agents. It provides the foundational patterns necessary to orchestrate multi-agent workflows, enabling models to perform complex, multi-step tasks across software engineering, customer support, and computer-use domains. The platform distinguishes itself through specialized capabilities for desktop and browser automation, allowing agents to interact with graphical interfaces by capturing visual context and executing precise mouse and keyboard inputs. It includes robust inf

    Identifies user emotional states to adjust agent behavior and improve support interactions.

    Python
    在 GitHub 上查看↗17,085
  • llmware-ai/llmwarellmware-ai 的头像

    llmware-ai/llmware

    14,838在 GitHub 上查看↗

    llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

    Executes specialized models for sentiment analysis and topic extraction.

    Python
    在 GitHub 上查看↗14,838
  • shengqiangzhang/examples-of-web-crawlersshengqiangzhang 的头像

    shengqiangzhang/examples-of-web-crawlers

    14,651在 GitHub 上查看↗

    This project is a collection of Python scripts and tools designed for web scraping, browser automation, and large-scale data extraction. It provides a set of implementations for retrieving information from websites and private APIs, including tools for multimedia downloading and social media data archiving. The toolset includes specialized mechanisms for bypassing anti-scraping measures through IP proxy pool rotation and multi-threaded crawlers. It also features capabilities for simulating browser sessions to handle authentication, intercepting session cookies, and decrypting network payloads

    Includes a feature to classify the emotional tone of incoming text to determine recipient mood.

    HTMLagent-poolcrawlerexample
    在 GitHub 上查看↗14,651
  • flairnlp/flairflairNLP 的头像

    flairNLP/flair

    14,378在 GitHub 上查看↗

    Flair is a transformer-based natural language processing framework used to build and train models for text classification and sequence tagging. It provides a specialized library for generating contextual text embeddings and performing linguistic analysis. The framework includes dedicated tools for named entity recognition, including the identification of specialized biomedical entities across multiple languages. It further supports entity linking to map identified text mentions to unique entries within general or biomedical knowledge bases. The project covers a broad range of language analys

    Ships capabilities for classifying the emotional tone of text as positive, negative, or neutral.

    Python
    在 GitHub 上查看↗14,378
  • zalandoresearch/flairzalandoresearch 的头像

    zalandoresearch/flair

    14,378在 GitHub 上查看↗

    Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl

    Implements capabilities to determine the emotional tone of sentences for classification as positive, negative, or neutral.

    Python
    在 GitHub 上查看↗14,378
  • apple/turicreateapple 的头像

    apple/turicreate

    11,171在 GitHub 上查看↗

    This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

    Provides general-purpose tools for classifying the emotional tone of text content.

    C++
    在 GitHub 上查看↗11,171
  • stanfordnlp/corenlpstanfordnlp 的头像

    stanfordnlp/CoreNLP

    10,085在 GitHub 上查看↗

    CoreNLP 是一个 Java 自然语言处理库,旨在将原始人类语言文本转换为结构化数据。它利用一套语言标注器通过流水线分析文本,提取语法结构、情感倾向和语言模式。 该项目包含一个指代消解引擎,用于链接同一实体的多个指代,以保持文档间的上下文一致性。它还提供了用于命名实体识别的工具,以对人名、公司和地点进行分类,以及一个词性标注器,用于为单词分配语法类别和词根形式。 该库还涵盖了文本情感分析,以评估情感基调,并支持将处理后的语言数据序列化为标准格式,以便进行外部存储或交换。

    Evaluates the emotional tone of text to determine if sentiment is positive, negative, or neutral.

    Java
    在 GitHub 上查看↗10,085
  • sloria/textblobsloria 的头像

    sloria/TextBlob

    9,516在 GitHub 上查看↗

    TextBlob is a natural language processing library that provides a unified interface for common linguistic tasks. It operates as a wrapper-based API, simplifying the use of complex processing libraries by delegating core operations to specialized external frameworks. The project features a pluggable processing pipeline that allows for the integration of custom logic and alternative language engines. It supports the extension of processing models through plugins to add specific language support or custom data processing. The library covers a broad range of linguistic capabilities, including se

    Calculates polarity and subjectivity to determine the emotional tone of text.

    Pythonnatural-language-processingnlpnltk
    在 GitHub 上查看↗9,516
  • rockyzsu/stockRockyzsu 的头像

    Rockyzsu/stock

    7,802在 GitHub 上查看↗

    This project is a quantitative trading platform and algorithmic trading bot designed for market data aggregation, strategy backtesting, and trade execution. It functions as a comprehensive system for collecting financial data via APIs and web sources, simulating investment strategies against historical records, and programmatically managing investment positions through brokerage interfaces. The platform distinguishes itself through institutional sentiment analysis and market intelligence tools. It monitors institutional fund activity, tracks corporate actions like equity pledges, and crawls f

    Analyzes financial news and institutional fund activity to identify market trends and sentiment.

    Pythonpythonquantstock
    在 GitHub 上查看↗7,802
  • anthropics/knowledge-work-pluginsanthropics 的头像

    anthropics/knowledge-work-plugins

    7,583在 GitHub 上查看↗

    This project is a plugin framework and agentic workflow library designed to connect large language models to professional toolstacks. It provides a system for integrating language models with external data warehouses, CRMs, and other enterprise software to retrieve and manipulate real-time business data. The framework enables the automation of specialized professional tasks through a file-based plugin definition system. It allows for the customization of domain expertise and plugin behavior to align with internal company processes, supported by an enterprise data connector that links models t

    Aggregates customer reviews and disputes into theme reports using sentiment analysis.

    Python
    在 GitHub 上查看↗7,583
  • 0xplaygrounds/rig0xPlaygrounds 的头像

    0xPlaygrounds/rig

    7,450在 GitHub 上查看↗

    Rig is a framework for building large language model applications, featuring a multi-provider client and a workflow builder for retrieval-augmented generation systems. It serves as an orchestrator for creating autonomous agents that can maintain conversation state and execute complex tasks through custom prompting and plugins. The project provides standardized interfaces for both completion and embedding model providers, allowing for unified request and response patterns across different engines. It also includes a vector database integration layer that defines a common interface for indexing

    Includes tools for identifying and categorizing the emotional tone of written input.

    Rustagentaiartificial-intelligence
    在 GitHub 上查看↗7,450
  • shekhargulati/52-technologies-in-2016shekhargulati 的头像

    shekhargulati/52-technologies-in-2016

    7,311在 GitHub 上查看↗

    This project serves as a comprehensive educational repository and technical reference collection, documenting a wide range of software engineering practices and modern development technologies. It provides a structured learning path for developers, curating tutorials and practical examples that cover the full lifecycle of application development, from initial project scaffolding to deployment and maintenance. The repository distinguishes itself by offering deep technical insights into complex architectural patterns, including actor-based concurrency models for managing parallel tasks and cont

    Provides general-purpose sentiment analysis tools for classifying text content as positive, negative, or neutral.

    JavaScriptawesomeawesome-listblog
    在 GitHub 上查看↗7,311
  • hulaspark/hulaHuLaSpark 的头像

    HuLaSpark/HuLa

    6,908在 GitHub 上查看↗

    HuLa is an open-source communication suite and cross-platform chat client designed for real-time instant messaging, voice, and video calls. It functions as an LLM-integrated messaging platform and an AI-powered social manager that synchronizes user data and settings across different operating systems. The platform integrates large language models to provide automated AI assistants, real-time message translation, and chat log summarization. It further utilizes artificial intelligence for user sentiment analysis to assist with social matching and risk management. The system includes a high-per

    Analyzes the emotional tone of messages in real time to aid social matching and risk management.

    Vuechatbotcross-platformcross-platform-app
    在 GitHub 上查看↗6,908
  • isnowfy/snownlpisnowfy 的头像

    isnowfy/snownlp

    6,631在 GitHub 上查看↗

    SnowNLP is a Python library for Chinese natural language processing. It provides tools for text segmentation, sentiment analysis, document classification, and phonetic transliteration. The library includes capabilities for training and saving custom machine learning models for tokenization and sentiment analysis using raw training datasets. It covers a range of linguistic processing areas, including parts of speech tagging, sentence splitting, and text similarity measurement. The toolkit also provides utilities for extracting key information through text summarization and calculating word im

    Evaluates the emotional tone of Chinese text to determine positive or negative sentiment.

    Python
    在 GitHub 上查看↗6,631
  • axa-group/nlp.jsaxa-group 的头像

    axa-group/nlp.js

    6,574在 GitHub 上查看↗

    nlp.js is a JavaScript natural language processing library and development framework used to build natural language understanding engines. It provides a toolkit for creating local machine learning models for intent classification and acts as a multilingual text processor that detects languages and normalizes text across various dialects. The framework distinguishes itself by supporting local execution on both servers and mobile devices, enabling chatbot functionality without an internet connection. It features a specialized system for conversational slot filling to collect mandatory informati

    Determines emotional tone across multiple languages using a predefined dictionary of weighted words.

    JavaScriptbotbotschatbot
    在 GitHub 上查看↗6,574
  • maxbbraun/trump2cashmaxbbraun 的头像

    maxbbraun/trump2cash

    6,522在 GitHub 上查看↗

    trump2cash is a sentiment-based stock trading bot and social media market monitor. It uses a natural language processing sentiment analysis tool to scan real-time social media feeds for mentions of publicly traded companies and translates the emotional tone of that text into automated buy or short stock market orders. The system utilizes a ticker mapping utility to resolve company names, subsidiaries, and brands into valid public stock market ticker symbols. To verify the efficacy of these sentiment-driven signals, it includes an algorithmic trading backtester that evaluates trading strategie

    Translates emotional tone scores from social media text into automated buy or short stock market orders.

    Python
    在 GitHub 上查看↗6,522
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  1. Home
  2. Artificial Intelligence & ML
  3. Sentiment Analysis Tools

探索子标签

  • Automated Sentiment Trends AnalysisAutomated processing of large text volumes to identify overarching sentiment trends. **Distinct from Automated Content Processing:** Distinct from general content processing: specifically targets the extraction of sentiment trends and intensity.
  • Contextual Sentiment EvaluationSentiment analysis that accounts for grammatical context such as negations and degree modifiers. **Distinct from Sentiment Analysis Tools:** Distinct from general tools by focusing specifically on the evaluation of contextual grammatical rules.
  • Derivative Sentiment AnalysisAnalyzing options open interest and strike scores to determine market sentiment for distant price targets. **Distinct from Sentiment Analysis Tools:** Focuses on sentiment derived from options market data rather than text-based emotional tone analysis.
  • Document Sentiment MappingTechniques for tracking sentiment shifts across different sections of a long document. **Distinct from Sentiment Analysis Tools:** Distinct from general tools by focusing on the mapping of sentiment changes throughout a document's structure.
  • Entity-Based ScoringSentiment analysis that assigns scores to specific identified business entities within a larger text block. **Distinct from Sentiment Analysis Tools:** Focuses on mapping sentiment to specific entities rather than general document or text polarity.
  • LLM-Based Sentiment AnalyzersAnalyzers that leverage large language models to interpret emotional tone and intent through contextual analysis. **Distinct from Lexicon-Based Sentiment Analyzers:** Distinct from Lexicon-Based Sentiment Analyzers: uses generative language models for contextual understanding rather than static word-weighting lists.
  • Lexicon-Based Sentiment Analyzers3 个子标签Analyzers that use predefined weighted word lists to determine sentiment ratios. **Distinct from Sentiment Classifiers:** Distinct from neural Sentiment Classifiers: relies on weighted lexicons rather than trained machine learning models.
  • Long-Form Sentiment AnalysisCapabilities for analyzing emotional tone across extended documents through sentence-level decomposition. **Distinct from Long-Form Text Generation:** Distinct from Long-Form Text Generation: focuses on analysis of existing text rather than generation.
  • Market Sentiment Analyzers1 个子标签Tools specialized in extracting emotional tone from financial news and social media to predict market movements. **Distinct from Sentiment Analysis Tools:** Distinct from general sentiment analysis tools by focusing specifically on financial domain data and market-driven labels
  • Social Media Sentiment AnalysisSentiment analysis specifically tuned for the nuances of short-form social media posts. **Distinct from Sentiment Analysis Tools:** More specific than general Sentiment Analysis Tools by targeting the linguistic patterns of social platforms.
  • Targeted Entity SentimentSentiment analysis that associates emotional polarity with specific entities mentioned in a text. **Distinct from Sentiment Analysis Tools:** Distinguishes targeted entity-level sentiment from general document-level sentiment analysis