Deep learning approach to text analysis for human emotion detection from big data

To properly assess the effects of the dataset origin in the task, for each combination of method and features, we tested on subsets of the dataset based on the sample origin and finally on the whole dataset. Xu et al. [12] has proposed an Emo2Vec method that encodes emotional semantics into vector form. They have trained Emo2Vec on a multitask learning framework by using smaller and larger datasets (smaller datasets such as ISEAR, WASSA, and Olympic). It shows that their results are better than those of Convolution Neural Network (CNN), DeepMoji embedding, and more. They have utilized their work on emotion analysis, sarcasm classification, stress detection, etc. Finally, the model Emo2Vec, when combined with Logistic Regression and GloVe, can achieve more competitive results.

Furthermore, our leave-one-out analysis demonstrated that our models can generalize to new data with reasonable accuracy. The CEASE dataset13, consisting of 2393 sentences extracted from around 205 English language suicide notes, collected from various websites and annotated for 15 emotion classes. For comparison purposes, they trained and tested various combinations of traditional ML classifiers (Multinomial-NB, RF, LR, and Support Vector Machine (SVC)) with different sets of features. The MLP ensemble and LTSM models achieved the best results, with an F1-Score of 59% on average for all classes, and 48% for guilt, 4 points less than the performance of the majority vote ensemble on this particular class.

Relational semantics (semantics of individual sentences)

Mood analysis also plays an important role in coaching salespeople to improve their conversational skills. About 80 per cent of all the data that can be collected in a sentiment analysis – whether by human or computer – is unstructured and eludes classical approaches to analysis. The targeted use of this data can mean an immense competitive advantage for companies or organisations.

Automatic methods, contrary to rule-based systems, don’t rely on manually crafted rules, but on machine learning techniques. A sentiment analysis task is usually modeled as a classification problem, whereby a classifier is fed a text and returns a category, e.g. positive, negative, or neutral. Research being done on natural language processing revolves around search, especially Enterprise search. This involves having users query data sets in the form of a question that they might pose to another person.

Syntax Analysis

These technologies help both individuals and organizations to analyze their data, uncover new insights, automate time and labor-consuming processes and gain competitive advantages. Natural language processing operates within computer programs to translate digital text from one language to another, to respond appropriately and sensibly to spoken commands, and summarise large volumes of information. Natural language processing is an aspect of everyday life, and in some applications, it is necessary within our home and work.

Then, the online approach classified streaming content of tweets in real time using the model developed in the offline approach [12]. In the field of sentiment analysis, the sentiment can be represented by emotions, attitudes, or opinions about objects or topics, and analysis focuses on the classification of based on emotions or an opinion polarity. We can say that we recognize emotion types in a text as a class them using a detection model. Rodriguez et al. [13] use emotion analysis to identify hate speech on social media. Their aim with this research was to locate and analyse the unstructured data of selected social media posts that intend to spread hate in the comment sections.

AI-driven text mining for emotion detection works in certain steps, which you can find below. Emotion detection gives companies a direction in which they should aim their advertising campaigns towards. The best way to reach audiences is to touch an emotional cord with them, and sentiment mining from customer feedback analysis can give you this insight readily. In word embedding, there are four methods, namely, word2vec, Global vectors for word representation (GloVe), Embedding from Language Models (ELMO), and fast text. The word associations from a large corpus using a neural network model [15].

There are a handful of sentiment analysis models that are different from one another and serve various purposes. There are lots of reasons why a company might use sentiment analysis tools. When a patient interacts with a healthcare organization over the phone related to their care, they are giving valuable feedback. The inability to review and learn from that feedback may be holding an organization back and preventing them from improving their offering as well as customer retention. The brand reputation use case made mention of how sentiment analysis can help you to have a more accurate net promoter score, but it’s worth taking a closer look at how it can improve your understanding of your NPS and Voice of Customer (VoC). What’s more, sentiment analysis can help you to filter incoming customer support tickets and ensure that they are labelled correctly, passed on to the appropriate team or department, and assigned the correct level of urgency.

Text communication via Web-based networking media, on the other hand, is somewhat overwhelming. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. It assesses whether the author has a negative, positive, or neutral attitude toward an item, administration, individual, or location.

Why Is Sentiment Analysis Important?

We will be scraping inshorts, the website, by leveraging python to retrieve news articles. We will be focusing on articles on technology, sports and world affairs. A typical news category landing page is depicted in the following figure, which also highlights the HTML section for the textual content of each article.

One of the main reasons for why it is excellent for text data processing is the development of word embeddings. Moreover, they capture the semantic relationships between words, which can be used as input to deep learning models. Convolutional and RNNs are widely used deep learning methods for text data processing. Machine learning represents a wide range of methods of which deep learning of neural networks is the most successful in text processing. Earlier approaches to natural language processing involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared.

Sentiment analysis use cases

They exploited sentiment and emotion scores to generate generalized and personalized recommendations for users based on their Twitter activity [4]. Sentiment or emotive analysis uses both natural language processing and machine learning to decode and analyze human emotions within subjective data such as news articles and influencer tweets. Positive, adverse, and impartial viewpoints can be readily identified to determine the consumer’s feelings towards a product, brand, or a specific service. Automatic sentiment analysis is employed to measure public or customer opinion, monitor a brand’s reputation, and further understand a customer’s overall experience. We introduce a novel Natural Language Processing (NLP) task called guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it.

• Data for emotion detection can be gathered from various sources depending on your objective.
• We will now build a function which will leverage requests to access and get the HTML content from the landing pages of each of the three news categories.
• Natural language processing and machine learning systems have only commenced their commercialization journey within industries and business operations.
• NLP techniques can be revolutionary when understanding employee sentiment and creating data-driven decisions in HR, but like all AI technologies, it has its limitations.
• Information spreads quickly via the Internet — a big part of it as text — and as we know, emotions tend to intensify if left undealt with.

The test puts forward the idea of the “Imitation Game”, a challenge that replaces the question ‘can machines think? ‘ and instead asks whether a machine can act indistinguishably from the way that a human does. Language – being the human vehicle of communication – is a key part of Turing’s test. Semantic search is an advanced information retrieval technique that aims to improve the accuracy and relevance of search results by… However, nowadays, AI-powered chatbots are developed to manage more complicated consumer requests making conversational experiences somewhat intuitive. For example, chatbots within healthcare systems can collect personal patient data, help patients evaluate their symptoms, and determine the appropriate next steps to take.

Approaches: Symbolic, statistical, neural networks

In those cases, companies typically brew their own tools starting with open source libraries. Advantages of NLP include efficient information retrieval, improved customer service through chatbots, accurate sentiment analysis, language translation, and creating more intuitive human-machine interfaces. The scope of NLP extends to numerous applications, including search engines, voice assistants, automated customer support, translation services, and sentiment analysis in social media.

• Emotion AI, also known as affective AI or affective computing, is a subset of artificial intelligence that analyzes, reacts to and simulates human emotions.
• What we’re starting to see for the first time is the melding of these two data streams.
• Or identify positive comments and respond directly, to use them to your benefit.
• There are also general-purpose analytics tools, he says, that have sentiment analysis, such as IBM Watson Discovery and Micro Focus IDOL.
• Lexical choice is only one way to encode sentiment, there are also grammatical patterns.

It involves the development of models and algorithms that enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Sentiment Analysis allows you to extract emotions and feelings in a given string of text. Also called Opinion Mining, it uses Natural Language Processing (NLP), text analysis and computational linguistics to identify and detect subjective information from the input text.

Figure 4 presents various techniques for sentiment analysis and emotion detection which are broadly classified into a lexicon-based approach, machine learning-based approach, deep learning-based approach. The hybrid approach is a combination of statistical and machine learning approaches to overcome the drawbacks of both approaches. Transfer learning is also a subset of machine learning which allows the use of the pre-trained model in other similar domain. Human language understanding and human language generation are the two aspects of natural language processing (NLP). The former, however, is more difficult due to ambiguities in natural language. However, the former is more challenging due to ambiguities present in natural language.

Additionally, these healthcare chatbots can arrange prompt medical appointments with the most suitable medical practitioners, and even suggest worthwhile treatments to partake. If you would like to explore how custom recipes can improve predictions; in other words, how custom recipes could decrease the value of LOGLOSS (in our current observe experiment), please refer to Appendix B. NLTK is widely used in academia and industry for NLP research, teaching NLP concepts, and developing NLP applications. It is well-documented and supported by an active community of developers and researchers.

The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer. Emotion detection with NLP represents a potent and transformative technology that augments our capacity to comprehend and respond effectively to human emotions. By scrutinizing textual data, speech, and even facial expressions, NLP models unearth valuable insights that extend across numerous domains, from customer service to mental health support. As NLP continues to advance, the trajectory of emotion detection promises even greater sophistication, further enriching our interactions with technology and each other. This journey is a testament to the remarkable synergy between human emotions and the technological prowess of NLP. NLP involves a variety of techniques, including computational linguistics, machine learning, and statistical modeling.

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