Rachit Singh
Anvi Staff answered 3 years ago
Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment analysis – otherwise known as opinion mining – is a much bandied about but often misunderstood term. In essence, it is the process of determining the emotional tone behind a series of words, used to gain an understanding of the the attitudes, opinions and emotions expressed within an online mention.
For beginners interested in sentiment analysis, there are several user-friendly tools available that require minimal technical expertise. Here are a few suitable options:
MonkeyLearn: MonkeyLearn offers user-friendly sentiment analysis tools with pre-built models and customizable features. It provides both text analysis and sentiment analysis capabilities. Website: https://monkeylearn.com/
TextBlob: TextBlob is a Python library that simplifies text processing tasks, including sentiment analysis. It’s easy to use and suitable for beginners with its straightforward API. Website: https://textblob.readthedocs.io/
IBM Watson Natural Language Understanding: IBM Watson NLU offers sentiment analysis among its many natural language processing features. It’s beginner-friendly with comprehensive documentation and easy-to-use APIs. Website: https://www.ibm.com/cloud/watson-natural-language-understanding
Google Cloud Natural Language API: Google Cloud NLP API provides sentiment analysis capabilities with pre-trained models. It offers easy integration with Google Cloud services and has extensive documentation for beginners. Website: https://cloud.google.com/natural-language
VADER (Valence Aware Dictionary and sEntiment Reasoner): VADER is a Python library specifically designed for sentiment analysis of social media texts. It’s easy to use and suitable for beginners due to its simplicity. Website: https://github.com/cjhutto/vaderSentiment
Stanford CoreNLP: Stanford CoreNLP is a natural language processing toolkit that includes sentiment analysis capabilities. It’s suitable for beginners with its comprehensive documentation and easy-to-use APIs. Website: https://stanfordnlp.github.io/CoreNLP/
Tweepy: Tweepy is a Python library for accessing the Twitter API, which can be used for sentiment analysis on Twitter data. It’s beginner-friendly and provides straightforward methods for collecting and analyzing tweets. Website: https://www.tweepy.org/
These tools offer intuitive interfaces, documentation, and support to help beginners get started with sentiment analysis quickly and effectively. Users can experiment with different tools to find the one that best suits their needs and preferences.
Sameer Staff answered 1 month ago
Sentiment analysis tools are software or platforms that use natural language processing (NLP) and machine learning to determine the emotional tone behind a body of text. These tools can help businesses, researchers, and individuals understand the sentiment (positive, negative, or neutral) expressed in customer feedback, social media posts, reviews, and other forms of text.
Here’s a list of some of the best sentiment analysis tools available today:
1. MonkeyLearn
Overview: MonkeyLearn is a machine learning platform that specializes in text analysis and offers pre-built sentiment analysis models. It allows users to easily classify text into categories, extract keywords, and perform sentiment analysis.
Key Features:
Customizable sentiment analysis models.
No coding skills required.
Integration with Google Sheets, Excel, and third-party applications.
Use Cases: Social media monitoring, product reviews analysis, customer feedback.
Website: MonkeyLearn
2. Lexalytics
Overview: Lexalytics provides enterprise-level text and sentiment analysis. It combines NLP, machine learning, and artificial intelligence to offer sophisticated analytics on unstructured text data.
Key Features:
Multilingual sentiment analysis.
Supports structured and unstructured data.
Contextual and theme-based analysis.
Use Cases: Media monitoring, market research, voice of the customer (VOC) analysis.
Website: Lexalytics
3. Hootsuite Insights (powered by Brandwatch)
Overview: Hootsuite Insights, integrated with Brandwatch, is a social media monitoring tool that provides real-time sentiment analysis for brands across social media channels.
Key Features:
Social media sentiment tracking.
Competitive analysis.
Visual sentiment dashboards and reports.
Use Cases: Brand reputation management, social media engagement, competitive monitoring.
Website: Hootsuite Insights
4. IBM Watson Natural Language Understanding
Overview: IBM Watson NLU provides deep insights into sentiment and emotion from text using AI-driven models. It supports analyzing text in multiple languages and formats.
Key Features:
Emotion and sentiment detection.
Entity, keyword, and concept extraction.
Integration with cloud and AI services.
Use Cases: Customer service analytics, news and content analysis, business intelligence.
Website: IBM Watson NLU
5. Google Cloud Natural Language API
Overview: Google’s Cloud Natural Language API offers advanced text analysis capabilities, including sentiment analysis, entity recognition, and syntax analysis. It’s ideal for developers looking to integrate sentiment analysis into their applications.
Key Features:
Supports multiple languages.
Easily integrates with other Google Cloud services.
Accurate sentiment scoring and magnitude analysis.
Use Cases: App development, social media analysis, product feedback.
Website: Google Cloud Natural Language
6. Brandwatch Consumer Research
Overview: Brandwatch is one of the leading social listening tools that offers sentiment analysis across a wide range of data sources, including social media, blogs, news sites, and more.
Key Features:
Real-time monitoring and reporting.
AI-powered sentiment and emotion analysis.
Competitive analysis and trend tracking.
Use Cases: Brand reputation, market research, crisis management.
Website: Brandwatch
7. HubSpot Service Hub
Overview: HubSpot offers sentiment analysis features through its customer service and feedback collection tools, allowing businesses to gather insights from surveys, emails, and social media conversations.
Key Features:
Automated ticket categorization based on sentiment.
Customer feedback tools with sentiment analytics.
Integration with other HubSpot CRM tools.
Use Cases: Customer service management, customer experience improvement, NPS tracking.
Website: HubSpot Service Hub
8. Awario
Overview: Awario is a social listening and sentiment analysis tool that helps brands track mentions, identify sentiment trends, and monitor brand reputation across social media, blogs, and websites.
Key Features:
Real-time sentiment analysis.
Boolean search for precise tracking.
Historical data analysis for sentiment trends.
Use Cases: Brand monitoring, competitor analysis, influencer marketing.
Website: Awario
9. Talkwalker
Overview: Talkwalker is a comprehensive social listening tool that uses AI-driven analytics to deliver real-time insights and sentiment analysis from social media, blogs, news, and forums.
Key Features:
Visual recognition of logos and images.
Multi-channel sentiment analysis.
Competitive benchmarking.
Use Cases: Crisis management, campaign performance tracking, audience engagement.
Website: Talkwalker
10. RapidMiner
Overview: RapidMiner is a data science and machine learning platform that offers a powerful text mining solution for sentiment analysis. It allows users to build custom models for extracting insights from large datasets.
Key Features:
Advanced text mining and sentiment analysis.
Customizable sentiment models.
Drag-and-drop interface for data processing.
Use Cases: Business intelligence, customer feedback analysis, academic research.
Website: RapidMiner
Sentiment analysis tools are essential for businesses and organizations looking to understand public opinion, customer feedback, and social media sentiment. The right tool depends on your specific use case, whether it’s social listening, customer service optimization, or advanced text mining. Many of these platforms offer customizable features and can be integrated into broader data analytics workflows, allowing for deeper insights and actionable results.