Aspect Level Sentiment Analysis using Machine Learning Approach: A Comprehensive Review

Sentimental analysis is now used from product marketing specific to the detection of social behavior. Progress on Facebook, Twitter, Youtube and other microblogging and social networking sites has not only contributed to a change in social sites, but also to the way we use these sites and the way we do it. People are fundamentally changing their feelings and their points of view with the general public. In this paper a detailed study of different approaches for lexicon-based sentiment analysis are discussed. This paper also shows that efficiency of machine learning over traditional lexicon based sentiment analysis

NEW MODERN APPROACH TO PREDICT USERS SENTIMENT USING CNN AND BLSTM

In Today’s world social network play a vital role and provides relevant information on user opinion. This paper presents emotional health monitoring system to detect stress and the user mood. Depending on results the system will send happy, calm, relaxing or motivational messages to users with phycological disturbance. It also sends warning messages to authorized persons in case a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long-term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorith

A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy

Text classification, the task of metadata to documents, needs a person to take significant time and effort. Since online-generated contents are explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Recently, various state-or-art text mining methods have been applied to classification process based on the keywords extraction. However, when using these keywords as features in the classification task, it is common that the number of feature dimensions is large. In addition, how to select keywords from documents as features in the classification task is a big challenge. Especially, when using traditional machine learning algorithms in big data, the computation time is very long. On the other hand, about 80% of real data is unstructured and non-labeled in the real world. The conventional supervised feature selection methods cannot be directly used in selecting entities from massive data. Usually, statistical strategies are utilized to extract features from unlabeled data for classification tasks according to their importance scores. We propose a novel method to extract key features effectively before feeding them into the classification assignment. Another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to documents. This problem makes text classification more complicated compared with a single label classification. For the above issues, we develop a framework for extracting data and reducing data dimension to solve the multi-label problem on labeled and unlabeled datasets. In order to reduce data dimension, we develop a hybrid feature selection method that extracts meaningful features according to the importance of each feature. The Word2Vec is applied to represent each document by a feature vector for the document categorization for the big dataset. The unsupervised approach is used to extract features from real online-generated data for text classification. Our unsupervised feature selection method is applied to extract depression symptoms from social media such as Twitter. In the future, these depression symptoms will be used for depression self-screening and diagnosis

Depression Detection Using Stacked Autoencoder from Facial Features and NLP

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods

Detecting Depression in Social Media : An Emotional Analysis Approach

Depression has been an ongoing mental health issue that has been affecting a wide range of humanity, particularly the young adults. To address and observe the more general public in a natural habitat, social media is examined for constructing a system to accurately detect depression. Despite the assiduous effort to construct a novel mechanism to detect depression from social media, behavioral approaches had underlying problems for users with a short activity span. To address this problem, emotion analysis was used as a tool to extract the emotion(s) of a user’s post to identify those with depression. Via machine learning techniques to construct an emotion classifier which in turn creates emotion embeddings for a binary classifier, this study proposes a pipeline structure to identify reddit posts from the depression subreddit. The model yielded promising results, introducing emotional analysis as a novel methodology in assessing mental health within social media

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns

Deep learning with knowledge graphs for fine-grained emotion classification in text

This PhD thesis investigates two key challenges in the area of fine-grained emotion detection in textual data. More specifically, this work focuses on (i) the accurate classification of emotion in tweets and (ii) improving the learning of representations from knowledge graphs using graph convolutional neural networks.The first part of this work outlines the task of emotion keyword detection in tweets and introduces a new resource called the EEK dataset. Tweets have previously been categorised as short sequences or sentence-level sentiment analysis, and it could be argued that this should no longer be the case, especially since Twitter increased its allowed character limit. Recurrent Neural Networks have become a well-established method to classify tweets over recent years, but have struggled with accurately classifying longer sequences due to the vanishing and exploding gradient descent problem. A common technique to overcome this problem has been to prune tweets to a shorter sequence length. However, this also meant that often potentially important emotion carrying information, which is often found towards the end of a tweet, was lost (e.g., emojis and hashtags). As such, tweets mostly face also problems with classifying long sequences, similar to other natural language processing tasks. To overcome these challenges, a multi-scale hierarchical recurrent neural network is proposed and benchmarked against other existing methods. The proposed learning model outperforms existing methods on the same task by up to 10.52%. Another key component for the accurate classification of tweets has been the use of language models, where more recent techniques such as BERT and ELMO have achieved great success in a range of different tasks. However, in Sentiment Analysis, a key challenge has always been to use language models that do not only take advantage of the context a word is used in but also the sentiment it carries. Therefore the second part of this work looks at improving representation learning for emotion classification by introducing both linguistic and emotion knowledge to language models. A new linguistically inspired knowledge graph called RELATE is introduced. Then a new language model is trained on a Graph Convolutional Neural Network and compared against several other existing language models, where it is found that the proposed embedding representations achieve competitive results to other LMs, whilst requiring less pre-training time and data. Finally, it is investigated how the proposed methods can be applied to document-level classification tasks. More specifically, this work focuses on the accurate classification of suicide notes and analyses whether sentiment and linguistic features are important for accurate classification

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot