Top 10 Best Tools, Libraries and Packages for Sentiment Analysis to Measure Brand Sentiment in 2020

How often do we receive a text from our friends and family and often get caught up or confused about what they intend to mean, if they are joking or being serious! As humans, we really don’t put up quite well over the tone and emotions behind text messages and this ultimately leads to not fully understanding the intent behind a message, making characters like sarcasm or wittiness hard to process! 

With the approach of online technology including social media, it was getting harder to determine the intent behind a message as there was no definite way to recognize the sentiment behind human interactions over text messages. Due to this underlying problem, it became a matter of concern for businesses as there was no proper way to decipher the sentiment behind customer reviews and to interpret whether there is a positive or negative correlation between the product/services and the customer experience. Considering the limitations such as word limit and distorted feedback, the customer service team lacked the information required in order to approach the query.

With the forthcoming of text-oriented communications like social media, there had to be an efficient way which could allow businesses to accurately analyse their brand sentiment and also the feedback from their customers. This paved a way for the use of a sentiment analysis tool which can effectively measure the sentiment behind text and also analyze the tone behind customer reviews and feedback from all over the internet. 

With that being said, let’s dive into what a sentiment analysis tool is and then move forward to our top 10 best tools for sentiment analysis in 2020.

Why use a Sentiment Analysis Tool?

To be precise, a sentiment analysis tool interprets text conversion, processes the tone behind the content and displays the accurate intent behind a message!  It thus takes the construct from user reviews and analyses the feedback into a measurable format on the basis of the language, tone and intent behind it. 

Brands use this information to evaluate the key areas of improvement and also engage with customer queries efficiently using all online mediums.

Sentiment Analysis for Brands

By using an automated framework to evaluate text-based communications, companies are able to discover how consumers actually feel about their goods, services and marketing strategies. 

By investing the time into a sentiment analysis software or tool, it not only gives the correct insight into understanding the ideal customer, but also saves a lot of valuable time for our team members. 

Instead of going through every customer feedback and responding one at a time, a sentiment analysis tool quickly analyses the input and interprets the data in the form of a neutral, positive or negative sentiment score and displays the analytics from analyzing these results in a visual format in the form of graphs and charts to better understand the trend over time in customer feedback. 

Having a basic perspective about how a sentiment analysis tool works and how it’s optimum for brands, let’s dive straight into our top 10 best sentiment analysis tools, libraries and packages in 2020.

Top 10 Best Sentiment Analysis Tools, libraries and packages

  • NLTK library for Python 
  • Spacy library for Python
  • Tidytext package for R 
  • Quanteda package for R
  • Lexalytics
  • Awario
  • Brandwatch
  • Repustate
  • Rosette
  • Sentiment Analyzer
1. Natural Language Toolkit for Python

First off, we got NLTK or better known as Natural Language Toolkit. It is the most well-known suite of libraries for general purpose NLP-based text classification in the Python programming ecosystem. It supports various third-party tools and extensions and has a fast tokenization system for sentences. It also supports a diverse range of languages in comparison to a few other ones. 

2. Spacy library for Python

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Next up is Spacy! Spacy is an open-source, free to use natural language processing library in Python. While working with a lot of text data, we need to have more context in hand in order to know about the intent or tone and Spacy does a really decent job in providing the right insights to these problems. It helps the user to create applications that can process a great volume of text data.   

3. Tidytext package for R

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Next up is Tidytext! Tidytext is an R package that is primarily used for text mining using a set of data principals. There are several sentiment lexicons that are provided within the Tidytext package out of which there are three major lexicons namely AFINN, bing and nrc. The following lexicons are based on one word unigrams and have a list of different words in English with each word having a positive or negative sentiment score. The nrc lexicon classifies terms into categories ranging from positive, negative, rage, excitement, disgust, fear, joy, disappointment, surprise, and belief in a binary format. The lexicon from bing categorizes words into positive and negative categories in a linear manner. The lexicon from AFINN assigns words with a score ranging from -5 to 5.

4. Quanteda package for R

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Quanteda is a package for R. It was designed to be used by individuals with textual data from books, tweets, or transcripts in order to handle the data and analyze its contents. It’s most common use is for content and sentiment analysis. The Quanteda package recognizes the text as three major components, namely corpus, dfm and tokens. 

5. Lexalytics

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Lexalytics is a powerful and intelligent tool which is based on natural language processing, NLP. It can analyze heaps of customer reviews or data, recognize the sentiment behind it and then interpret the correct output to the business! It does this with the help of its well known ‘salience’ engine. This helps businesses understand why a customer is feeling a certain way! Lexalytics includes a very large dictionary of sentiment phrases in five different languages. Such scores are defined by how much a given sentence occurs in the vicinity of a collection of recognized positive terms (eg, nice, fantastic, spectacular) and a collection of poor words (eg, terrible, horrific, awful). This program determines the emotional phrases in a text, scores these phrases and then combines them to determine a sentence’s overall feeling.

6. Awario

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Next up is Awario! Now, Awario is a tool that is efficient in monitoring the web and social media networks. There comes an inbuilt sentiment analysis feature with it which is great for sorting the sentiment based on positive, negative or neutral. It displays a graph showing how the sentiment has changed over time for a particular brand. It also displays the topics related to the brand or company that the users are most likely to talk about.

7. Brandwatch

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Brandwatch is another great tool that can tell businesses what their customer feedback is all about. This tool calculates the sentiment based on a large library which recognizes the structure of a particular sentence or what word phrases have been used. They support over 27 languages and their sentiment analysis is quite impressively accurate. It’s incredibly useful for the analysis of long term trends and can alert the user about sudden spikes in one sentiment type. If you don’t agree with a classification, you can change it in the system either manually or using automated rules.

8. Repustate

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Repustate is an easy to use machine learning platform which is highly customizable. Their API provides scoped sentiment analysis which allows the user to get multiple scores based on the text provided. The software can easily recognize short term text, emojis and slangs. The user interface looks quite bizarre and sophisticated, but provides the user a complete control over the analyzation process. Repustate has tailored various techniques that can deliver a quick and to the point text analytics ecosystem.

9. Rosette

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The primary feature that sets apart Rosette from the other tools is the ability to analyze text in more than 30 different languages. It analyzes the feelings in terms of positive, negative and neutral and can detect the sentiment in one mention identified by its entityID.

10.  Sentiment Analyzer

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The final tool on our list is the Sentiment Analyzer! It is the most simple, free-to-use tool on our list and what makes it even better is just the ease of its use. It enables the user to directly copy the content that needs to be analyzed and put it inside the text analyzer. After clicking on the “Analyze Text” button, it will show the sentiment score based on the text. It does this using text mining technology and computational linguistics. This makes it an easy to use tool for a business looking to quickly analyze the sentiment behind any text messages in any format.

Do share it around if it provided some insight!

References

https://www.kdnuggets.com/2018/07/comparison-top-6-python-nlp-libraries.html

https://www.tidytextmining.com/sentiment.html

http://www2.imm.dtu.dk/pubdb/pubs/6010-full.html

https://joss.theoj.org/papers/10.21105/joss.00037.pdf

https://spacy.io/usage/spacy-101

https://support.rosette.com/hc/en-us/articles/204743635-Sentiment-Analysis

https://blog.hubspot.com/service/sentiment-analysis-tools

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