Empower Your Sentiment Analysis with Essential Resources

Unlock the potential of sentiment analysis with a comprehensive collection of resources. Explore tools, guides, and tips to enhance your sentiment analysis capabilities. You will also find here links towards various lists of positive words and lists of negative words to use in your assignments or projects.

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique that involves analyzing and determining the sentiment or emotional tone expressed in a piece of text, such as a review, comment, or social media post. The primary goal of sentiment analysis is to understand whether the expressed sentiment is positive, negative, or neutral.

Here’s how sentiment analysis typically works:

1. Text Data Collection: First, you gather a dataset containing text data that you want to analyze for sentiment. This data could be customer reviews, social media posts, news articles, or any other form of textual content.
2. Text Preprocessing: Before analysis, the text data often undergoes preprocessing steps, including tokenization (breaking text into individual words or phrases), removing stopwords (common words like “the,” “and,” “is”), and stemming or lemmatization (reducing words to their base form).
3. Sentiment Classification: The core of sentiment analysis involves classifying each piece of text into one of several categories, typically “positive,” “negative,” or “neutral.” More advanced sentiment analysis may include additional categories like “very positive” or “very negative.”
4. Machine Learning or Rule-Based Approach: Sentiment analysis can be done using machine learning techniques, where a model is trained on labeled data to predict sentiment, or through rule-based approaches, where predefined rules and lexicons are used to assign sentiment scores to words and phrases.
5. Sentiment Scoring: In machine learning-based approaches, a sentiment score is assigned to each piece of text. This score can be a numerical value indicating the intensity of sentiment (e.g., a score of +0.8 for strongly positive sentiment). Rule-based approaches often use sentiment lexicons to determine sentiment scores.
6. Aggregation and Visualization: Sentiment analysis results can be aggregated and visualized to provide insights into overall sentiment trends, such as sentiment over time or sentiment within specific topics.

Sentiment analysis has various practical applications, including:

• Business Insights: Companies use sentiment analysis to understand customer opinions about their products or services by analyzing reviews and social media feedback.
• Market Research: It helps gauge public sentiment about products, brands, or political candidates.
• Customer Support: Sentiment analysis can be used to automatically classify and prioritize customer support requests based on the sentiment expressed in messages.
• Social Media Monitoring: It’s used for tracking the sentiment around a brand or topic on social media platforms.
• Financial Markets: Sentiment analysis is applied to analyze news articles and social media data to predict market trends.

Overall, sentiment analysis is a valuable tool for extracting meaningful insights from large volumes of textual data and understanding the emotions and opinions of individuals or groups.

Resources for Sentiment Analysis:

1. Semantria

Semantria applies Text and Sentiment Analysis to tweets, Facebook posts, surveys, reviews, or enterprise content. See also these links Resources, Excel, Demo

Lexalytics acquired Semantria in 2014 and added their cloud text/sentiment analysis API and Excel plug-in to their product stack.

2. Lexalytics

State-of-the-art technologies to turn unstructured text into useful data. Hundreds of F1000 companies rely on Lexalytics text mining results. Lexalytics Resources

See more information about Sentiment Analysis Explained at Lexalytics.

3. Sentiment Analysis Dictionaries

Check out these Dictionaries!

At the University of Pittsburgh, they have Sentiment Lexicon. It’s a lexicon of about 8,000 words with positive/neutral/negative sentiment. It’s described in more detail in this paper and released under the GPL.

Professor Bing Liu provides an English Lexicon of about 6800 words that you can download, You can also use it for Opinion Mining and Opinion Spam Detection.

This paper from 2002 describes an algorithm for deriving such a dictionary from text samples automatically, using only two words as a seed set.

4. Meaning Cloud

Find more here https://www.meaningcloud.com/

MeaningCloud is the easiest, most powerful and most affordable way to extract the meaning of all kind of unstructured content: social conversations, articles, documents…

5. Wikipedia Resources

6. The Stanford Natural Language Processing Group

Sentiment analysis at The Stanford Natural Language Processing Group with a Live Demo that is loading very hard.

7. Alchemy

Text and sentiment analysis is performed also by Alchemy, which is an IBM company. See the Alchemy Resources and Sentiment Analysis API

AlchemyAPI’s sentiment analysis algorithm looks for words that carry a positive or negative connotation then figures out which person, place or thing they are referring to. It also understands negations (i.e. “this car is good” vs. “this car is not good”) and modifiers (i.e. “this car is good” vs. “this car is really good”). The sentiment analysis API works on documents large and small, including news articles, blog posts, product reviews, comments and Tweets.

8. Online downloadable PDF

Here is an interesting online downloadable pdf about Introduction to Sentiment Analysis

9. SAS

You can also go and check the resources from SAS Sentiment Analysis

10. Python NLTK

Sentiment Analysis with Python NLTK Text Classification Live Demo

11. Downloadable list of positive words or list of negative words:

• List of negative words: GitHub negative-words.txt
• List of positive words: Positive Words Research: online + downloadable excel, GitHub positive-words.txt
• SentiWordNet (https://sentiwordnet.isti.cnr.it/) is an excellent publicly available lexicon. Technically, the resource contains Princeton WordNet data marked with polarity scores. (Check its licensing policy before using).
• Hu and Liu’s lexicon: Another option is available at https://www.cs.uic.edu/~liub/FBS/… (look for the section called “Opinion Lexicon”.
• Sentiment lexicon (https://www.cs.pitt.edu/mpqa/) 8221 words scored for polarity (positive or negative), subjectivity. Distinguishes between POS tags.
• General inquirer (https://www.wjh.harvard.edu/~inqu…) has several dictionaries, e.g., a “positive” list with 1’915 words and one ‘negative’ list with 2’291 words
• Affective word list https://www.sci.sdsu.edu/CAL/word…
• Bing Liu and Minquing Hu at the University of Illinois at Chicago have curated a bunch of sentiment datasets. Opinion Mining, Sentiment Analysis, Opinion Extraction
• Not a database but a list of  Negative (-ve) words and adjectives list for sentiment analysis 
• For Negative words jeffreybreen/twitter-sentiment-analysis-tutorial-201107 and for positive words
  jeffreybreen/twitter-sentiment-analysis-tutorial-201107

In conclusion, sentiment analysis is a powerful tool in the realm of natural language processing that enables us to decipher the sentiments and emotions expressed in text data. Whether it’s understanding customer feedback, tracking public opinion, or predicting market trends, sentiment analysis provides valuable insights that can inform decision-making and strategy development across various domains.

As technology continues to advance, sentiment analysis is becoming increasingly sophisticated, with machine learning techniques and extensive sentiment lexicons improving accuracy and nuance in sentiment classification. This evolution is empowering businesses, researchers, and individuals to harness the power of sentiment analysis for a wide range of applications.

In today’s data-driven world, the ability to gauge sentiment accurately is a crucial asset. By delving into the emotional tone of text data, we can better understand the needs and preferences of customers, anticipate shifts in public opinion, and make more informed choices in various sectors. As the field of sentiment analysis continues to grow, it promises to play an even more significant role in shaping the way we interact with and respond to the vast amounts of textual information available to us.

Do you know other sentiment analysis resources? Share below.