An Introduction to Sentiment Analysis Using NLP and ML

nlp sentiment analysis

This can be a powerful analytic tool that helps product teams make better informed decisions to improve products, customer relations, agent training, and more. Product teams at telephony companies use Sentiment Analysis to extract the sentiments of customer-agent conversations via cloud-based contact centers. Then, these teams can track customer feelings and feedback toward particular products, events, or even agents, aiding customer service. Interested in building tools that intelligently tracking how interviewees feel about certain topics? Or tools that monitor how customers feel toward a new product across all social media mentions?

Is NLP the same as sentiment analysis?

Sentiment analysis is a subset of Natural Language Processing (NLP). It is a data mining technique that measures and tries to understand people's opinions and stances through NLP. Computational linguistics and text analysis inspect information from the web, social media, and many other online sources.

This method employs a more elaborate polarity range and can be used if businesses want to get a more precise understanding of customer sentiment/feedback. The response gathered is categorized into the sentiment that ranges from 5-stars to a 1-star. Namely, it tells you why customers feel the way that they do, instead of how they feel. Using its analyzeSentiment feature, developers will receive a sentiment of positive, neutral, or negative for each speech segment in a transcription text. Each text segment will also be assigned a magnitude score that indicates how much emotional content was present for analysis.

Sentiment analysis tools

The result of sentiment analysis can be an average score of overall positivity, a word cloud of the most popular words in a text or a detailed analysis of associations that can be inferred from the data. Let’s say that you are analyzing customer sentiment using fine-grained analysis. You want to identify the particular aspect or features for which people are mentioning positive or negative reviews. Now you can have real people on your data analytics team review the data and tweak it if necessary. They can update the algorithm if they notice obvious misinterpretations of the data.

  • Further, these feature vectors generate the predicted tags like positive, negative, and neutral.
  • Additionally, tracking online reputation over time can help you identify trends and make data-driven decisions.
  • In contrast to classical methods, sentiment analysis with transformers means you don’t have to use manually defined features – as with all deep learning models.
  • We take cutting-edge technological concepts and break them down into bite-sized pieces for everyday business people.
  • GPU-accelerated DL frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++.
  • Done right, it can be a great value-added to your systems, apps, or web projects.

As you can see, sentiment analysis can provide meaningful results for companies and organizations in virtually any sector or industry. It can improve your understanding of your business and customers and increase efficiency and performance. These emotional guidelines help the AI model to understand the context of the sentiments being expressed.


Usually, when analyzing sentiments of texts you’ll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way. Many emotion detection systems use lexicons (i.e. lists of words and the emotions they convey) or complex machine learning algorithms. As mentioned in the introduction, we will use a subset of the Yelp reviews available on Hugging Face that have been marked up manually with sentiment. We’ll use Kibana’s file upload feature to upload a sample of this data set for processing with the Inference processor. In the first example, the word polarity of “unpredictable” is predicted as positive.

Is sentiment analysis of NLP an application?

Sentiment analysis is one of the most used applications of NLP. It identifies and extracts views using spoken or written language.

You can review your product online and compare them to your competition. You can also analyze the negative points of your competitors and use them to your advantage. A satisfying customer experience means a higher chance of returning the customers.

Pros And Cons Of Sentiment Analysis

In this paper an algorithm for encryption & decryption of digital image using chaotic logistic map and Arnold cat map is discussed. The algorithm utilizes the good features of chaotic sequence related to cryptographic properties, such as pseudo-random, sensitivity to initial conditions and aperiodicity. The algorithm use logistic mapping to confusion the location of pixels in a digital image & Arnold cat map parameters are to be considered as secret keys for securing an image. Due to change of any secret keys the system produces undesired results at the receiver side. The Machine Learning Algorithms usually expect features in the form of numeric vectors. Hence, after the initial preprocessing phase, we need to transform the text into a meaningful vector (or array) of numbers.

nlp sentiment analysis

This allows the AI model to understand the fundamental grammatical structure of the text, but not really the text itself. For example, sentences can be grammatically correct and not make any sense, or it could fail to identify the contextual use of some words as a result of the sentiment or emotion within the text (sarcasm being a common issue). How sentiment analysis works, Lettria’s approach to sentiment analysis, and some key use cases.

Predicting Sentiments Using Pre-Trained Models

The text can be classified into general categories such as positive, negative, neutral, or even more nuanced categories. Some advanced techniques can even detect emotions, such as happiness, sadness, anger, or fear. Deep learning (DL) is a subset of machine learning (ML) that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as NLP and others. DL word embedding techniques such as Word2Vec encode words in meaningful ways by learning word associations, meaning, semantics, and syntax.

nlp sentiment analysis

The author is a post-graduate scholar and researcher in the field of AI/ML who shares a deep love for Web development and has worked on multiple projects using a wide array of frameworks. He is also a FOSS enthusiast and actively contributes to several open source projects. He blogs at, where he shares valuable insights and tutorials on emerging technologies. The Naïve Bayes algorithm is a probabilistic classifier used for predictive analysis. It is simpler as compared to other algorithms and has been known to have a higher success rate. Naïve Bayes makes the assumption that all input attributes are conditionally independent.

Introduction to Sentiment Analysis: Concept, Working, and Application

But if you feed a machine learning model with a few thousand pre-tagged examples, it can learn to understand what “sick burn” means in the context of video gaming, versus in the context of healthcare. And you can apply similar training methods to understand other double-meanings as well. Rules-based sentiment analysis, for example, can be an effective way to build a foundation for PoS tagging and sentiment analysis. This is where machine learning can step in to shoulder the load of complex natural language processing tasks, such as understanding double-meanings. In contrast to classical methods, sentiment analysis with transformers means you don’t have to use manually defined features – as with all deep learning models. You just need to tokenize the text data and process with the transformer model.

For instance, the most common words in a language are called stop words. They are generally irrelevant when processing language, unless a specific use case warrants their inclusion. The strings() method of twitter_samples will print all of the tweets within a dataset as strings. Setting the different tweet collections as a variable will make processing and testing easier. If you would like to use your own dataset, you can gather tweets from a specific time period, user, or hashtag by using the Twitter API. Look across your company for all the customer feedback data sources to integrate into your analysis platform.

Predictions on unseen reviews

With the increasing volume of user-generated content on the internet, businesses are leveraging sentiment analysis machine learning techniques to gain valuable insights and improve decision-making. Online reputation is the perception of your brand based on what people say and share about you on the internet. It can affect your sales, customer loyalty, brand awareness, and trust. To manage your online reputation effectively, you need to monitor and measure the sentiment of your online mentions, reviews, ratings, social media posts, and other sources of feedback.

nlp sentiment analysis

What is sentiment analysis in Python using NLP?

What is Sentiment Analysis? Sentiment Analysis is a use case of Natural Language Processing (NLP) and comes under the category of text classification. To put it simply, Sentiment Analysis involves classifying a text into various sentiments, such as positive or negative, Happy, Sad or Neutral, etc.

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