In this work, we provide a follow-up on the Moldavian versus Romanian Cross-Dialect Topic Identification (MRC) shared task of the VarDial 2019 Evaluation Campaign. The shared task included two sub-task types: one that consisted in discriminating between the Moldavian and the Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, e.g. the top model for Moldavian versus Romanian dialect identification obtained a macro F1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared to machine learning (ML) models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, e.g. when we shorten the text samples to single sentences or when use tweets at inference time. A secondary goal of our work is to propose an improved ML model using ensemble learning. Our experiments show that ML models can accurately identify the dialects, even at the sentence level and across different domains (news articles versus tweets). We also analyze the most discriminative features of the best performing models, providing some explanations behind the decisions taken by these models. Interestingly, we learn new dialectal patterns previously unknown to us or to our human annotators. Furthermore, we conduct experiments showing that the machine learning performance on the MRC shared task can be improved through an ensemble based on classifier stacking.

The Unreasonable Effectiveness of Machine Learning in Moldavian versus Romanian Dialect Identification

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online To analyze these large quantities of data, a special classification method known as sentiment analysis, is used This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts By using machine learning techniques, sentiment analysis achieved a great success around the world This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data

Sentiment analysis in social media using machine learning techniques

We apply a deep neural network supervised learning (DNN) approach to extract text topics from analyst reports based on whether the topics are used to justify the quantitative numbers (justification type), such as the target price, or not (qualitative type). A baseline model without using text information has an adjusted R squared of 2.3% in predicting the cumulative two-day abnormal returns. When we include the topic tones, the adjusted R squared increases to 15.4%. This significant increase of R squared is mainly driven by the qualitative type topics and not much driven by the justification type topics.

Topic tones of analyst reports and stock returns: A deep learning approach

Sentiment Analysis is a widely used text classification technique. It breaks down any given text or comments and classify the text either as positive or negative based on the views conveyed in it. Previous works done on sentiment classification used either lexicon based approach or machine learning techniques. Likewise, the major drawback of the existing systems was the focus on only binary classification of review such as positive or negative. Ignorance of the neutral review will result in misinterpretation of a customer’s opinion about a product or movie, which will degrade the business or trend. In case of using only lexicon based approach, the system highly depends on the selection of lexicon resource and dictionary. In case of system built only using machine learning approach, the performance of the system depends on the algorithms chosen. This work presents a hybrid model to resolve the neutral class too. The proposed work combines a lexical approach (SentiWordNet) with the machine learning algorithms such as Support Vector Machine, Decision Tree, Logistic Regression and Naive Bayes for sentiment analysis to resolve the neutral opinions beyond the binary categorization of the customer’s review. We have also compared the performance of these four machine learning algorithms along with the lexicon approach. The results proved that Support Vector Machine and Logistic Regression algorithms outperform the other two algorithms with an accuracy of about 80% which is on average differs by 6% to 10% when compared to other algorithms.

Sentiment Analysis for Predicting Customer Reviews using a Hybrid Approach

This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naive Bayes were modeled for sentiment and emotion analyses, whereas latent Dirichlet allocation was administered to identify top emerging topics based on English textual reviews from three digital payment applications. Random forest produced the best results for sentiment (F1 score = 73.8%; Cohen’s Kappa = 52.2%) and emotion (F1 score = 58.8%; Cohen’s Kappa = 44.7%) analyses based on a tenfold cross-validation. Latent Dirichlet allocation revealed best clusters at k = 5 and items = 25, with the top topics being App Service, Transaction, Reload Features, Connectivity and Reward. Findings are presented and discussed in general and also based on each application.

A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

Sentiment analysis is the analysis of emotions and opinions from any form of text. Sentiment analysis is also termed as opinion mining. Sentiment analysis of the data is very useful to express the opinion of the mass or group or any individual. This technique is used to find the sentiment of the person with respect to a given source of content. Social media and other online platforms contain a huge amount of the data in the form of tweets, blogs, and updates on the status, posts, etc. In this paper, we have analyzed the Movie reviews using various techniques like Naïve Bayes, K-Nearest Neighbour and Random Forest.

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Sentiment Analysis of Movie Reviews using Machine Learning Techniques

In network communication, attackers often breach the security. Therefore, keeping the data and servers secure is a very crucial task. Among several online attacks, DDOS is the most devastating attack. This attack has the most ravaging effect on the servers. There exists a tremendous pressure on security experts to mitigate the annihilating effects of this attack. In this paper, we have done a comprehensive research on types of DDOS attacks and mitigating its effects. Albeit this attack cannot be fully curbed, it can be extenuated to a certain extent.

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DDOS and Compilation of Mitigation Techniques

Election plays an essential role in choosing the leader and deciding the future of the country for next few years. The proliferation of micro-blogging messages or tweets around the elections can be used to predict the sentiments of a person. Using text analysis different opinions and emotions can be identified and that concept is known as Sentiment Analysis or Opinion Mining. As the UP elections were completed and a lot of tweets were available in the research, live tweets were collected for five days during the elections. After collecting the tweets various operations were performed on the tweets and then analysis was done on the tweets to identify the sentiments of the people after election. The tweets were collected specifically related Mr. Yogi Adityanathth.

Palak Baid

Papers

Sentiment Analysis of Movie Reviews using Machine Learning Techniques

DDOS and Compilation of Mitigation Techniques

Sentiment Analysis of Live Tweets After Elections