Sentiment Classification Problem Research Articles

An Efficient Aspect-based Sentiment Classification with Hybrid Word Embeddings and CNN Framework

Background: As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product. Objectives: In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases. Methods: Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification. Results: Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches. Conclusion: In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

Optimizer algorithms and convolutional neural networks for text classification

<span lang="EN-US">Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems.</span>

Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification

With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in the field of natural language processing and data mining in recent ten years. The paper starts with the topic of "Sentiment Analysis using a CNN-BiLSTM deep model based on attention mechanism classification". First, it conducts an in-depth investigation on the current research status and commonly used algorithms at home and abroad, and briefly introduces and analyzes the current mainstream sentiment analysis methods. As a direction of machine learning, deep learning has become a hot research topic in emotion classification in the field of natural language processing. This paper uses deep learning models to study the sentiment classification problem of short and long text sentiment classification tasks. The main research contents are as follows. Firstly, Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. The feature dimension is too high, and the feature information of the pool layer is lost, which leads to the loss of the details of the emotion vocabulary. To solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined in Quora dataset. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long- term dependencies between words hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. Secondly, we propose an attention based CNN-BiLSTM hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism in IMDB movie reviews dataset. In the experiment, under the control of single variable of Data volume and Epoch, the proposed hybrid model was compared with the results of various indicators including recall, precision, F1 score and accuracy of CNN, LSTM and CNN-LSTM in long text. When the data size was 13 k, the proposed model had the highest accuracy at 0.908, and the F1 score also showed the highest performance at 0.883. When the epoch value for obtaining the optimal accuracy of each model was 10 for CNN, 14 for LSTM, 5 for MLP and 15 epochs for CNN-LSTM, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.906, and accuracy of the proposed model was the highest at 0.929. Finally, the experimental results show that the bidirectional long- and short-term memory convolutional neural network (BiLSTM-CNN) model based on attention mechanism can effectively improve the performance of sentiment classification of data sets when processing long-text sentiment classification tasks. Keywords: sentiment analysis, CNN, BiLSTM, attention mechanism, text classification

A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification

Over the last decade, many methods have been developed to address the domain dependency problem of sentiment classification under domain shift. This problem is exacerbated in Arabic by its feature sparsity induced by morphological complexity and dialect variability. However, only a few studies have proposed sentiment domain adaptation methods for Arabic, with inconsistent comparisons resulting from different datasets and settings, making it difficult to identify the most effective approaches. This is the first comparative study of the most effective domain adaptation methods for Arabic sentiment classification. We replicate the existing methods proposed for Arabic and compare their effectiveness on the standard dataset settings. To further examine the extent to which adaptation performance differs between Modern Standard Arabic (MSA) and Dialectal Arabic (DA), we employ two public multi-domain sentiment datasets. We also test two well-established methods that have been thoroughly utilized in English-related studies and examine if they maintain the same levels of performance when applied to Arabic. Our findings indicate that adaptation performanace on MSA is better than on DA for all traditional approaches. However, implementing adaptation on top of transformer-based language models shows superior performance on DA. Finally, methods that have proven to excel in English suffer from low performance when applied to Arabic and exhibit negative transfer in most cases.

A Hybrid Learning Particle Swarm Optimization With Fuzzy Logic for Sentiment Classification Problems

Methods based on deep learning have great utility in the current field of sentiment classification. To better optimize the setting of hyper-parameters in deep learning, a hybrid learning particle swarm optimization with fuzzy logic (HLPSO-FL) is proposed in this paper. Hybrid learning strategies are divided into mainstream learning strategies and random learning strategies. The mainstream learning strategy is to define the mainstream particles in the cluster and build a scale-free network through the mainstream particles. The random learning strategy makes full use of historical information and speeds up the convergence of the algorithm. Furthermore, fuzzy logic is used to control algorithm parameters to balance algorithm exploration and exploration performance. HLPSO-FL has completed comparison experiments on benchmark functions and real sentiment classification problems respectively. The experimental results show that HLPSO-FL can effectively complete the hyperparameter optimization of sentiment classification problem in deep learning and has strong convergence.

Semi-supervised adversarial discriminative domain adaptation.

Domain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods train the model on training data and test that model on a completely separate dataset. The adversarial-based adaptation method became popular among other domain adaptation methods. Relying on the idea of GAN, the adversarial-based domain adaptation tries to minimize the distribution between the training and testing dataset based on the adversarial learning process. We observe that the semi-supervised learning approach can combine with the adversarial-based method to solve the domain adaptation problem. In this paper, we propose an improved adversarial domain adaptation method called Semi-Supervised Adversarial Discriminative Domain Adaptation (SADDA), which can outperform other prior domain adaptation methods. We also show that SADDA has a wide range of applications and illustrate the promise of our method for image classification and sentiment classification problems.

A novel activation function based recurrent neural networks and their applications on sentiment classification and dynamic problems solving.

In this paper, a nonlinear activation function (NAF) is proposed to constructed three recurrent neural network (RNN) models (Simple RNN (SRNN) model, Long Short-term Memory (LSTM) model and Gated Recurrent Unit (GRU) model) for sentiment classification. The Internet Movie Database (IMDB) sentiment classification experiment results demonstrate that the three RNN models using the NAF achieve better accuracy and lower loss values compared with other commonly used activation functions (AF), such as ReLU, SELU etc. Moreover, in terms of dynamic problems solving, a fixed-time convergent recurrent neural network (FTCRNN) model with the NAF is constructed. Additionally, the fixed-time convergence property of the FTCRNN model is strictly validated and the upper bound convergence time formula of the FTCRNN model is obtained. Furthermore, the numerical simulation results of dynamic Sylvester equation (DSE) solving using the FTCRNN model indicate that the neural state solutions of the FTCRNN model quickly converge to the theoretical solutions of DSE problems whether there are noises or not. Ultimately, the FTCRNN model is also utilized to realize trajectory tracking of robot manipulator and electric circuit currents computation for the further validation of its accurateness and robustness, and the corresponding results further validate its superior performance and widespread applicability.

TaskDrop: A competitive baseline for continual learning of sentiment classification

In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i.e., a model is sequentially trained to classify the sentiment of reviews of products in a particular category. The use of common sentiment words in reviews of different product categories leads to large cross-task similarity, which differentiates it from continual learning in other domains. This knowledge sharing nature renders forgetting reduction focused approaches less effective for the problem under consideration. Unlike existing approaches, where task-specific masks are learned with specifically presumed training objectives, we propose an approach called Task-aware Dropout (TaskDrop) to randomly sample a binary mask for each task. While the standard dropout generates and applies random masks for each training instance per epoch for regularization, random masks in TaskDrop are used for model capacity allocation and reuse to each coming task. We conducted experimental studies on Amazon review data and made comparison to various baselines and state-of-the-art approaches. Our empirical results show that regardless of simplicity, TaskDrop overall achieved competitive performance, especially after relatively long term learning. This demonstrates that the proposed random capacity allocation mechanism works well for continual sentiment classification.

Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification

Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem.

Comparative Analysis Using Machine Learning Techniques for Fine Grain Sentiments

Huge amount of data is being produced every second for microblogs, different content sharing sites, and social networking. Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them. It is widely used for social media platforms to find user's sentiments about a particular topic or product. Capturing, assembling, and analyzing sentiments has been challenge for researchers. To handle these challenges, we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank (SST) dataset, based on 215,154 exclusive texts of different lengths that are manually labeled. We present comparative sentiment analysis to solve the fine-grained sentiment classification problem. The proposed approach takes start by pre-processing the data and then apply eight machine-learning algorithms for the sentiment classification namely Support Vector Machine (SVM), Logistic Regression (LR), Neural Networks (NN), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Adaboost and Naïve Bayes (NB). On the basis of results obtained the accuracy, precision, recall and F1-score were calculated to draw a comparison between the classification approaches being used.

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

Sentiment Classification of News Text Data Using Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews

Traditionally, pharmacovigilance data are collected during clinical trials on a small sample of patients and are therefore insufficient to adequately assess drugs. Nowadays, consumers use online drug forums to share their opinions and experiences about medication. These feedbacks, which are widely available on the web, are automatically analyzed to extract relevant information for decision-making. Currently, sentiment analysis methods are being put forward to leverage consumers' opinions and produce useful drug monitoring indicators. However, these methods' effectiveness depends on the quality of word representation, which presents a real challenge because the information contained in user reviews is noisy and very subjective. Over time, several sentiment classification problems use machine learning methods based on the traditional bag of words model, sometimes enhanced with lexical resources. In recent years, word embedding models have significantly improved classification performance due to their ability to capture words' syntactic and semantic properties. Unfortunately, these latter models are weak in sentiment classification tasks because they are unable to encode sentiment information in the word representation. Indeed, two words with opposite polarities can have close word embeddings as they appear together in the same context. To overcome this drawback, some studies have proposed refining pre-trained word embeddings with lexical resources or learning word embeddings using training data. However, these models depend on external resources and are complex to implement. This work proposes a deep contextual word embeddings model called ELMo that inherently captures the sentiment information by providing separate vectors for words with opposite polarities. Different variants of our proposed model are compared with a benchmark of pre-trained word embeddings models using SVM classifier trained on Drug Review Dataset. Experimental results show that ELMo embeddings improve classification performance in sentiment analysis tasks on the pharmaceutical domain.

Bullet Subtitle Sentiment Classification Based on Affective Computing and Ensemble Learning

The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some traditional affective computing methods only consider the text information and ignore the information of other dimensions, we construct a bullet subtitle affective computing method by combining the information of other dimensions of the bullet subtitle. Finally, aiming at the problem that existing classification algorithms ignore the importance of sentiment words in short texts, we propose a sentiment classification method based on affective computing and ensemble learning. Our experiment results show that the proposed method has higher accuracy and better practical application effect.

RAFFMAN: Measuring and Analyzing Sentiment in Online Political Forum Discussions with an Application to the Trump Impeachment

Given an online forum, how can we quantify changes in user affect towards a person or an idea over time? We argue that online political forums constitute an untapped opportunity for understanding sentiment toward aspects under discussion. However, the analysis of such forums has received little attention from the research community. In this paper, we develop RAFFMAN, a systematic approach to quantify the impact of external events on the affect of forum users towards a concept, such as a person or an entity. First, we develop an approach to capture and quantify the observed activity: we identify related keywords, filter threads, and establish correlations between events and spikes in the activity. Second, we modify and evaluate state-of-the-art NLP techniques to achieve high accuracy (74%) in a three-class sentiment classification problem. As a case study, we deploy our method to quantify the effect of President Trump’s impeachment on several concepts including: President Trump, Speaker Pelosi, and QAnon. Our data consists of 32M posts from Reddit and 4chan over a span of 6 months from September 2019 to February 2020. This initial analysis hints at an increase in political polarization, especially for people’s affect towards the President. Overall, our work is a building block towards mining the affect of online forum user towards a concept, which constitutes a untapped, massive, and publicly-available source of information.

An attention network based on feature sequences for cross-domain sentiment classification

The difficulty of cross-domain text sentiment classification is that the data distributions in the source domain and the target domain are inconsistent. This paper proposes an attention network based on feature sequences (ANFS) for cross-domain sentiment classification, which focuses on important semantic features by using the attention mechanism. Particularly, ANFS uses a three-layer convolutional neural network (CNN) to perform deep feature extraction on the text, and then uses a bidirectional long short-term memory (BiLSTM) to capture the long-term dependency relationship among the text feature sequences. We first transfer the ANFS model trained on the source domain to the target domain and share the parameters of the convolutional layer; then we use a small amount of labeled target domain data to fine-tune the model of the BiLSTM layer and the attention layer. The experimental results on cross-domain sentiment analysis tasks demonstrate that ANFS can significantly outperform the state-of-the-art methods for cross-domain sentiment classification problems.

RESIDUAL ATTENTION BI-DIRECTIONAL LONG SHORT-TERM MEMORY FOR VIETNAMESE SENTIMENT CLASSIFICATION

Sentiment classification is a problem of assessing and estimating values of people’s opinions, sentiments, and attitudes to products, services, individuals, and organizations. Sentiment analysis helps companies understand their customers for improving marketing strategies in e-commerce, manufacturers decide how to improve their products, or people adjust behavior in their lives. In this paper, we propose a deep network model to classify the reviewed product in Vietnamese. Specifically, we develop a new deep learning model called the Residual Attention Bidirectional-Long Short Term Memory (ReAt-Bi-LSTM) model. First, the residual technique is used in multiple layers Bidirectional Long Short Term Memory (Bi-LSTM), to enhance the model’s capability in learning high-level features from input documents. Second, the attention mechanism is integrated after the last Bi-LSTM layer to assess each word’s contribution to the context vector to the document’s label. Last, the document’s final representation is the combination of the context vector and output of Bi-LSTM. This representation captures both context information from the context vector and sequence information from the Bi- LSTM network. We conducted extensive experiments on four common Vietnamese sentiment datasets. The results show that our proposed model improves the accuracy compared with some baseline methods and one state of the art model for the sentiment classification problem.

An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.

Twitter Sentiment Analysis using Deep Learning

In this report, address the problem of sentiment classification on twitter dataset. used a number of machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority vote ensemble method with 5 of our best models to achieve the classification accuracy of 83.58% on kaggle public leaderboard. compared various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a CSV file of type tweet_id, sentiment, tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in . Similarly, the test dataset is a CSV file of type tweet_id, tweet. Please note that CSV headers are not expected and should be removed from the training and test datasets. used Anaconda distribution of Python for datasets for library requirements specific to some methods such as keras with TensorFlow backend for Logistic Regression, MLP, RNN (LSTM), and CNN. and xgboost for XGBoost. Usage of preprocessing, baseline, Naive Bayes, Maximum entropy, Decision Tree, random forest, multi-layer perception etc are implemented.