Effective classification of natural language texts and determination of speech tonality using selected machine learning methods

Pleshakova Ekaterina Sergeevna ORCID: 0000-0002-8806-1478 PhD in Technical Science Associate Professor, Department of Information Security, Financial University under the Government of the Russian Federation 125167, Russia, Moscow, 4th Veshnyakovsky Ave., 12k2, building 2 espleshakova@fa.ru Other publications by this author     Gataullin Sergei Timurovich PhD in Economics Dean of "Digital Economy and Mass Communications" Department of the Moscow Technical University of Communications and Informatics; Leading Researcher of the Department of Information Security of the Financial University under the Government of the Russian Federation 8A Aviamotornaya str., Moscow, 111024, Russia stgataullin@fa.ru Other publications by this author     Osipov Aleksei Viktorovich PhD in Physics and Mathematics Associate Professor, Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation 125167, Russia, Moscow, 4th veshnyakovsky str., 4, building 2 avosipov@fa.ru Other publications by this author     Romanova Ekaterina Vladimirovna PhD in Physics and Mathematics Associate Professor, Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation 125167, Russia, Moscow, 49/2 Leningradsky Ave. EkVRomanova@fa.ru Other publications by this author

Samburov Nikolai Sergeevich Student, Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation 125167, Russia, Moscow, 49/2 Leningradsky Ave. ncsamburov@mail.ru

The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).

Text classification has gained huge popularity in recent years, and has found application of this technology in various applications, such as the classification of emotions from text, spam classification and many others.

With the advent of social networks, people have become more likely to express their emotions through their activities on social networks.  People convey their emotions through social media posts, such as comments on their own or someone else's posts, reviews, blogs. The machine can process different types of data ^[1-3]. For example, services such as YouTube and TikTok work with photos and videos. Algorithms analyze your actions: both the topic and the direction of the video, the language, and how fully you watch the video are taken into account. The machine analyzes this data, and based on it recommends new videos that you are likely to like. Also, with the help of other signs, the machine can be trained to recognize a person in a video or photo. Such technologies are widely used around the world to ensure security: the prevention or investigation of crimes.

All modern music streaming services, such as Spotify and Yandex.Music, which contain a "Recommendations" section, work on a similar principle. The machine analyzes the mood of the song, its genre, language and popularity.

Many manufacturers of goods and services also use machine learning to collect and analyze feedback from their customers in order to further improve their product and stand out favorably from competitors. In such cases, the text of the reviews is processed, as well as other factors, such as whether a refund has been issued. One of the other applications of machine learning in text classification tasks is machine translation of natural languages ^[4-5].

In this work, the training of models was carried out on the text data of publications on the social network Twitter on the topic “COVID-19”. In this paper, the authors consider the problem of determining the tonality of the text. The main task of analyzing the tonality of the text is to determine the emotional fullness and coloring.         

To perform text classification tasks, there are the most suitable methods: logistic regression, multilayer perceptron, random forest, naive Bayesian method, K-nearest neighbor method, decision tree, stochastic gradient descent.

Models are evaluated using various indicators - reliability of predictions, accuracy, completeness and F-score. Also, one of the tasks is to use technologies to improve the quality of models.

Text classification The task of determining the tonality of the text is identical to other tasks of text classification.

The machine analyzes the text, highlighting keywords, their semantic load and significance for determining mood, on the basis of which it makes its conclusion. The tonality of texts can be divided into three main groups - negative, positive and neutral, where the latter means the absence of emotional coloring in the text. To solve this problem, the data set "Coronavirus tweets NLP" was selected ^[6-7].           

Since the machine recognizes keywords when classifying texts, it would be advisable to create a "word cloud" for the texts of all publications of the dataset.

In order to correctly train the model and obtain clean results, it is necessary to clear the text of unnecessary characters and meaningless words. The first step is to remove punctuation marks, numbers, "@" and "#" characters, as well as the HTTP protocol text. A significant part of meaningless words are so-called "stop words".

            After cleaning the processed text, we compile a dictionary for further research. 1. Tokenization of the text           

Creating a "word cloud" for the cleared data:

Fig. 2. "Word cloud" for the cleared data To simplify further classification, classes from the "Mood" column are replaced with numeric values from 1 to 5, where 1 is "Extremely negative" and 5 is "Extremely positive":

Model training. Sample separation and text vectorization.The next task is to encode words in sentences with numerical representations.

To do this, you can create a dictionary of all the unique words that occur in the dataset and associate each of the words in the sentence with this dictionary. In this case, each sentence should be presented as a list, the length of which will be equal to the size of the dictionary of unique words (53553 words in our case), and each index of such a list will store how many times a word occurs in this sentence. This method is called a "Bag of words" ^[8]. The bag of words model (BoW model) is a shortened and simplified representation of a text document from selected parts of the text based on certain criteria, such as the frequency of words. The size of the training sample was 32925 elements, the test sample was 8232.

Logistic regression

One of the simplest and most common tools in text classification is logistic regression. Logistic regression falls under the algorithm of controlled classification ^[9]. This algorithm has gained importance recently, and its use has increased significantly.

The training time of the model was 16.6 seconds. Metrics are poor, 57% accuracy of predictions suggests that the model was wrong in almost half of the cases.

            According to the results obtained, we see that the worst indicators are when predicting positive and negative publications.

Fig. 3. Error matrix. Logistic regression (5)

The result obtained arose due to the fact that logistic regression is difficult to work in cases where the target contains more than 2 values. Let's try to reduce the number of classes and compare the results. To do this, we will combine "Extremely positive" and "Positive" publications under one label. We will do the same operation with negative publications and set the gradation from 0 to 2, where 0 is negative reviews, and 2 is positive.

After changing the labels, the class distribution looks like this:

Fig. 4. Distribution of classes after combining labels           

After the merger, we will conduct re-training. The results have improved significantly (accuracy of ?80% is considered a good indicator). The training time was 9.7 seconds.             A detailed report showed that the model predicts positive publications best.

Fig. 5. Error matrix. Logistic regression (3)

Since the classification of texts is considered in the work, some words may be more important for the distribution of the text into one or another category. This problem is solved by the TF-IDF (Term Frequency – Inverse Document Frequency) method for text vectorization. The TF-IDF technique is usually used to extract features and to define terms that occur repeatedly in the text. TF-IDF does not reappear in general for each collection of documents. The selection of related keywords and the definition of the method are used to encode these keywords in supervised machine learning. These keywords can have a huge impact on the ability of classification methods to extract the best pattern.  This method determines how often a word occurs in various documents and, based on this, assigns significance to the words for classification ^[10]. To do this, apply TfidfVectorizer on top of an already existing "Bag of words":

Now, for example, let's build a new logistic regression and compare the results. The result shows that the indicators increased slightly, by about 1%. When training further models, both the usual "Bag of Words" and with the use of TF-IDF will be used.

Multilayer Perceptronthe most traditional type of neural network architecture is the multilayer perceptron network (MLP).

This is a class of artificial neural networks with direct communication, consisting of at least three layers of nodes: an input layer, a hidden layer and an output layer.  A perceptron is a block that receives one or more input data (values of independent variables in the model), subjects each input to a unique linear mathematical transformation, including multiplication by a weighting parameter, summation of weighted input data, addition of an offset parameter, and processing. the resulting value of the activation function, which then serves as the output of the perceptron block ^[11].  The MLPClassifier classifier is used to train the multilayer perceptron model:           

The training time of the model is 3 minutes and 30 seconds.

· Accuracy ?79% is a good indicator· Precision ?79% is a good indicator· Recall ?79% is a good indicator· F-score ?79% is a good indicator. 6. Error matrix.

Multilayer perceptron with a "Bag of words"

With the use of TF-IDF           

The model was trained in 3 minutes 53 seconds.

· Accuracy ?78% is a good indicator· Precision ?78% is a good indicator

· Recall ?78% is a good indicator· F-score ?78% is a good indicator Indicators fell by about 1% compared to the results of a multilayer perceptron with a "Bag of words" Fig.

7. Error matrix. Multilayer perceptron with TF-IDF Random Forest           

A random forest works with decision trees that are used to classify a new object from an input vector.  This algorithm builds a large number of decision trees because they work together. Decision trees act as pillars in this algorithm. A random forest is defined as a group of decision trees whose nodes are determined at the preprocessing stage ^[12]. After building several trees, the best feature is selected from a random subset of features. To train a Random Forest model, the RandomForestClassifier classifier is used. The model was trained in 51 seconds· Accuracy ?75% is a good indicator· Precision ?75% is a good indicator· Recall ?75% is a good indicator· F-score ?75% is a good indicator. 8. Error matrix.

Random forest with a "Bag of words" With TF-IDF, the model learned in 46 seconds.

· Accuracy ?20% is a very bad indicator· Precision ?80% is a good indicator· Recall ?20% is a very bad indicator· F-score ?8% is a very bad indicator The use of TF-IDF has degraded the quality of the model so much that it cannot be used to perform classification tasks.

Fig. 9. Error matrix. Random forest with TF-IDFMODEL defined almost all documents as "Neutral".

Naive Bayesian method The naive Bayes algorithm is used for pattern recognition and classification, which fall under various variants of image classifiers for basic probability and likelihood ^[13].

  The MultinominalNB classifier is used to train the Naive Bayesian model. The model was trained in 0.009 seconds.

· Accuracy ?67% - average· Precision ?69% - average· Recall ?67% - average· F-score ?65% - average

Fig. 10. The error matrix. Naive Bayesian method with the "Bag of words"

WITH TF-IDF

The model was studied in 0.01 second.

· Accuracy of ?63% - average

· Precision of ?70% - average

· Recall ?63% - average

· F-score ?56% - bad record

Performance deteriorated by approximately 4% compared with the Naive Bayes model with the "Bag of words". The greatest losses in the metric F-score – 9%

Fig. 11. The error matrix. Naive Bayes with TF-IDF

The method of K-nearest neighbors

This algorithm focuses on the conservation of similar things closer to each other. This model works with class labels and feature vectors in the data set ^[14]. KNN stores all cases, and helps to classify new cases, with the help of measures of similarity. In K-nearest neighbors the text is represented using a spatial vector, which is denoted by S = S ( T1 , W1 ; T2 , W2, ... Tn , Wn ). For any text using the training text is calculated similarity, and selected texts with the greatest similarity. Finally, the classes are determined on the basis of the K neighbors. To train the model by the method of K-nearest neighbors classifier is used KNeighborsClassifier:

The model was trained for 0,003 sec.

· Accuracy of ?31% - very poor indicator

· Precision of ?65% of the average

· Recall ?31% - very poor indicator

· F-score ?29% is a very poor indicator

Fig. 12. The error matrix. The method of K-nearest neighbors with a "Bag of words"

CTF-IDF

            Due to some characteristics of the classifier in this case, we can't build a model by the method of K-nearest neighbors using TF-IDF.

Decision tree

The decision tree generates a set of rules that can be used to categorize data according to a set of attributes and their classes. To train the model by the method of decision Tree classifier is used DecisionTreeClassifier. The model was trained for 8 seconds.

· Accuracy of ?70% - average

· Precision of ?70% - average

· Recall ?70% - average

· F-score ?70% - average

Fig. 13. The error matrix. The decision tree with the "Bag of words"

WITH TF-IDF

The model was trained for 9 seconds.

· Accuracy ?61% - average

· Precision of ?62% - average

· Recall ?62% - average· F-score ?62% - average The indicators deteriorated by about 8% compared to the Decision Tree model with a "Bag of words".

Fig. 14. Error matrix. Decision Tree with TF-IDF Stochastic Gradient Descent Algorithm is very similar to traditional gradient descent.

 However, it only calculates the loss derivative of one random data point, not all data points (hence the name, stochastic). This makes the algorithm much faster than gradient descent. The SGDClassifier classifier is used to train the model by stochastic gradient descent. The model was trained in 0.226 seconds.

· Accuracy ?82% is a very good indicator· Precision ?82% is a very good indicator· Recall ?82% is a very good indicator· F-score ?82% is a very good indicator. 15. Error matrix.

Stochastic gradient descent with a "Bag of words"

With TF-IDF           

The training of the model was 0.127 seconds.

· Accuracy ?77% is a good indicator· Precision ?77% is a good indicator· Recall ?77% is a good indicator· F-score ?76% is a good indicator The indicators deteriorated by about 5% compared to the stochastic gradient descent model with a "Bag of words".

Conclusion           

This article provides an extensive and systematic overview of various machine learning methods for determining the tonality of speech from the point of view of natural language processing. Actions such as: data set analysis, creation of descriptive graphs and statistical information, data set preprocessing, feature extraction and text vectorization, model construction, improvement of the quality of constructed models are implemented. Below is a table that contains summary information about the results of training models using the methods discussed in the paper:

Fig. 16. Comparative table of models Color designations in the table:

1. Green color – improvement of the indicator of the model or a separate metric.

2.      Red color - deterioration of the indicator of the model or a separate metric.

3.      The blue color is the model with the best indicators.

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