Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data Scientific Reports

The NLTK library contains various utilities that allow you to effectively manipulate and analyze linguistic data. Among its advanced features are text classifiers that you can use for many kinds of classification, including sentiment analysis. Yes, sentiment analysis is a subset of AI that analyzes text to determine emotional tone (positive, negative, neutral). Accuracy is defined as the percentage of tweets in the testing dataset for which the model was correctly able to predict the sentiment.

The proportion of positive cases that were accurately predicted is known as precision and is derived in the Eq. From the output, you can see that our algorithm achieved an accuracy of 75.30. Bag of words scheme is the simplest way of converting text to numbers. For example, a reviewer might say they are “not happy.” Although the word happy is included in the review, the preceding word indicates that the consumer is anything but. We have created this notebook so you can use it through this tutorial in Google Colab. Out of all the NLP tasks, I personally think that Sentiment Analysis (SA) is probably the easiest, which makes it the most suitable starting point for anyone who wants to start go into NLP.

Sentiment Analysis Challenges

In conclusion, sentiment analysis is a crucial tool in deciphering the mood and opinions expressed in textual data, providing valuable insights for businesses and individuals alike. By classifying text as positive, negative, or neutral, sentiment analysis aids in understanding customer sentiments, improving brand reputation, and making informed business decisions. Why would you use this method and not any other different and more simple?

Multi-class sentiment analysis categorizes text into more than two sentiment categories, such as very positive, positive, very negative, negative and neutral. Since multi-class models have many categories, they can be more difficult to train and less accurate. These systems often require more training data than a binary system because it needs many examples of each class, ideally distributed evenly, to reduce the likelihood of a biased model. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services.

Analyze emotion

The simplest implementation of sentiment analysis is using a scored word list. Understanding public approval is obviously important in politics, which makes sentiment analysis a popular tool for political campaigns. A politician’s team can use sentiment analysis to monitor the reception of political campaigns and debates, thereby allowing candidates to adjust their messaging and strategy. We can also use sentiment analysis to track media bias in order to gauge whether content evokes a positive or negative emotion about a certain candidate. Sentiment analysis is used throughout politics to gain insights into public opinion and inform political strategy and decision making.

Once the reviews are in a computer-readable format, we can use a sentiment analysis model to determine whether the reviews reflect positive or negative emotions. The basic level of sentiment analysis involves either statistics or machine learning based on supervised or semi-supervised learning algorithms. As with the Hedonometer, supervised learning involves humans to score a data set. With semi-supervised learning, there’s a combination of automated learning and periodic checks to make sure the algorithm is getting things right. In NLP, computational linguistics—rule-based human language modeling—is integrated with statistical, machine learning, and deep learning models.

You need the averaged_perceptron_tagger resource to determine the context of a word in a sentence. Sentiment analysis in NLP is about deciphering such sentiment from text. Now comes the machine learning model creation part and in this project, I’m going to use Random Forest Classifier, and we will tune the hyperparameters using GridSearchCV. ‘ngram_range’ is a parameter, which we use to give importance to the combination of words, such as, “social media” has a different meaning than “social” and “media” separately. But, for the sake of simplicity, we will merge these labels into two classes, i.e. As the data is in text format, separated by semicolons and without column names, we will create the data frame with read_csv() and parameters as “delimiter” and “names”.

Using sentiment analysis, policymakers can, ideally, identify emerging trends and issues that negatively impact their constituents, then take action to alleviate and improve the situation. In the same way we can use sentiment analysis to gauge public opinion of our brand, we can use it to gauge public opinion of our competitor’s brand and products. If we see a competitor launch a new product that’s poorly received by the public, we can potentially identify the pain points and launch a competing product that lives up to consumer standards. NLP libraries capable of performing sentiment analysis include HuggingFace, SpaCy, Flair, and AllenNLP.

So, the model performs well for sentiment analysis when compared to other pre-trained models. Empirical study was performed on prompt-based sentiment analysis and emotion detection19 in order to understand the bias towards pre-trained models applied for affective computing. The findings suggest that the number of label classes, emotional label-word selections, prompt templates and positions, and the word forms of emotion lexicons are factors that biased the pre-trained models20. The Dravidian Code-Mix-FIRE 2020 has been informed of the sentiment polarity of code-mixed languages like Tamil-English and Malayalam-English14.

Offensive targeted other is offense or violence in the comment that does not fit into either of the above categories8. The class labels of offensive language are not offensive, offensive targeted insult individual, offensive untargeted, offensive targeted insult group and offensive targeted insult other. The total number of texts in each category is represented in Table 3. It is evident from the output that for almost all the airlines, the majority of the tweets are negative, followed by neutral and positive tweets.

We will also remove the code that was commented out by following the tutorial, along with the lemmatize_sentence function, as the lemmatization is completed by the new remove_noise function. You also explored some of its limitations, such as not detecting sarcasm in particular examples. Your completed code still has artifacts leftover from following the tutorial, so the next step will guide you through aligning the code to Python’s best practices. To summarize, you extracted the tweets from nltk, tokenized, normalized, and cleaned up the tweets for using in the model. Finally, you also looked at the frequencies of tokens in the data and checked the frequencies of the top ten tokens. Since we will normalize word forms within the remove_noise() function, you can comment out the lemmatize_sentence() function from the script.

NLTK provides a number of functions that you can call with few or no arguments that will help you meaningfully analyze text before you even touch its machine learning capabilities. Many of NLTK’s utilities are helpful in preparing your data for more advanced analysis. The analysis revealed an overall positive sentiment towards the product, with 70% of mentions being positive, 20% neutral, and 10% negative.

It has Recurrent neural networks, Long short-term memory, Gated recurrent unit, etc to process sequential data like text. Over here, the lexicon method, tokenization, and parsing come in the rule-based. The approach is that counts the number of positive and negative words in the given dataset. If the number of positive words is greater than the number of negative words then the sentiment is positive else vice-versa. You will use the negative and positive tweets to train your model on sentiment analysis later in the tutorial.

• Machine language and deep learning approaches to sentiment analysis require large training data sets.
• In our case, it took almost 10 minutes using a GPU and fine-tuning the model with 3,000 samples.
• The surplus is that the accuracy is high compared to the other two approaches.
• The reason for this misclassification which the proposed model predicted as having a untargeted category.
• The best NLP solutions follow 5 NLP processing steps to analyze written and spoken language.
• If you are curious to learn more about how these companies extract information from such textual inputs, then this post is for you.

The model prediction function outputs unnormalized probability scores. To find the class probabilities we take a softmax across the unnormalized scores. The class with the highest class probabilities is taken to be the predicted class.

The following function makes a generator function to change the format of the cleaned data. There are certain issues that might arise during the preprocessing of text. For instance, words without spaces (“iLoveYou”) will be treated as one and it can be difficult to separate such words. Furthermore, “Hi”, “Hii”, and “Hiiiii” will be treated differently by the script unless you write something specific to tackle the issue. It’s common to fine tune the noise removal process for your specific data. Subsequently, the method described in a patent by Volcani and Fogel,[5] looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales.

We can even break these principal sentiments(positive and negative) into smaller sub sentiments such as “Happy”, “Love”, ”Surprise”, “Sad”, “Fear”, “Angry” etc. as per the needs or business requirement. In this article, is sentiment analysis nlp we will focus on the sentiment analysis of text data. We will iterate through 10k samples for predict_proba make a single prediction at a time while scoring all 10k without iteration using the batch_predict_proa method.

Data are however available from the authors upon reasonable request and with permission of24. It is split into a training set which consists of 32,604 tweets, validation set consists of 4076 tweets and test set consists of 4076 tweets. The dataset contains two features namely text and corresponding class labels.