Description:Field of the Invention
The proposed invention involves the use of advanced techniques from the domains of deep learning, natural language processing, and data analysis to automatically detect and analyze emotions expressed within text, as well as provide responses based on these emotions. This invention has applications in various industries including Customer Service and Feedback Analysis, Social Media Monitoring, Educational Technology, Mental Health Support, Content Creation and Marketing, Market Research and Product Development, HR and Employee Feedback.
Objectives of the invention
The primary objective of this is to introduce a novel system and method for utilizing deep learning to accurately detect emotions in text and provide insightful feedback along with utilizing the customer care manpowereffectively. By harnessing advanced neural network techniques within natural language processing, this invention aims to enhance emotion analysis across industries like customer service, education, social media, and more.The main goal is to offer a scalable solution for business organizations that goes beyond current methods, improving decision-making, and emotional understanding of their customers.Through cutting-edge technology integration, the invention seeks to transform how emotions are perceived and leveraged in text-based communication.
Background of the Invention
This innovation arises from the need to accurately decipher emotions in digital text. Traditional methods fall short in capturing the complexity of human sentiment expressed online. This invention recognizes the potential of deep learning, especially neural networks, in revolutionizing emotion analysis by leveraging their success in various domains. The surge in digital communication emphasizes the demand for automated tools to process vast amounts of textual data swiftly and meaningfully. The invention's foundation lies in combining deep learning's capabilities with the growing importance of artificial intelligence and natural language processing, enabling accurate emotion detection and valuable feedback provision. This addresses challenges in understanding emotions, enhancing decision-making, and improving various aspects of digital interaction.
For instance, CN112597759A discloses a text-based emotion detection method and device, computer equipment and a medium, wherein the emotion detection method comprises the following steps: respectively inputting the text data into N trained neural network language models and outputting N emotion prediction probabilities, wherein each neural network language model comprises a pre-training language model, a first full-link layer and an activation function; and fusing the N emotion prediction probabilities to obtain the final prediction probability of the text data, wherein N is a natural number which is more than or equal to 2. This system needs additional equipment and a medium.
Similarly, US8412530B2 is a method for automatically detecting sentiments in an audio signal of an interaction held in a call centre, including receiving the audio signal from a logging and capturing unit. Performing audio analysis on the audio signal to obtain text spoken within the interaction. Segmenting the text into context units according to acoustic information acquired from the audio signal to identify units of speech bound by non-speech segments, wherein each context unit includes one or more words. But this method works only for voice-based applications.
CN112527968A provides a composition review method and system based on a neural network, and relates to the field of natural language processing. The method comprises the following steps: acquiring a document to be evaluated; obtaining a characteristic evaluation result of the document to be evaluated according to the plurality of characteristic information of the document to be evaluated and the preset deep neural network; and according to the characteristic evaluation result, carrying out weighted summation to obtain a document evaluation result of the document to be evaluated. The composition review method and the composition review system can investigate the quality of the document to be reviewed from multiple dimensions. This method is only used to generate reviews about the document. CN108009297Binvention proposes a method and system for analyzing the emotions in text using natural language processing. The method first collects the text to be analyzed. Then, it performs semantic scene analysis on the text to identify the emotion and theme of the text. Next, the method uses an emotional vocabulary comparison table to determine the emotional tendency of the main body of the text and the theme of the text. Finally, the method determines the emotional tendency of the text based on the emotional tendency of the main body of the text and the theme of the text. This invention is just a starting point of our invention with an inaccurate result giving tendency.
US10565244B2 proposes a system and method for improved categorization and sentiment analysis which is fed textual data such as transcriptions or collated data from a network enabled service, or some other source, which then segments textual data into chunks, It then categorizes the text chunks using a combination of regular expression rules, semantic similarity, and semantic clustering. The sentiment of each category is then analyzed. The results of the analysis are output to the user in the form of text that can be used for the purpose of taking actions to improve business outcomes.It is merely tasked with categorizing the sentiment of the given text which is divided into chunks.
In the disclosures, as mentioned earlier, In the digital age, textual communication has become a central part of our lives, spanning social media, customer reviews, emails, educational platforms, and more. However, accurately gauging the emotions underlying these texts is challenging, as humans convey a wide range of sentiments and subtle nuances that are not easily captured by rule-based systems or basic sentiment analysis algorithms. This invention capitalizes on artificial intelligence technological progress to create a system that not only detects emotions but also offers actionable insights. By understanding the emotional context of text, organizations can make informed decisions, improve customer experiences, enhance content creation, and even contribute to mental health support. This invention is rooted in the challenges of interpreting emotions in text-based communication and the opportunity presented by deep learning techniques to create a more accurate, nuanced, and actionable system for emotion detection and feedback analysis. It seeks to address the limitations of traditional methods and leverage technological advancements to transform the way emotions are understood and utilized in digital interactions.
Summary of the Invention
The invention, "Emotion Recognition from Text and Feedback Analysis using Deep Learning" introduces a pioneering system that leverages advanced deep learning techniques to accurately detect emotions within digital text and offer valuable feedback based on these emotions. Unlike conventional methods that struggle to capture nuanced sentiments, this invention harnesses the power of neural networks and natural language processing to revolutionize emotion analysis in various applications. The background includesthe limitations of existing methods, and the potential of AI and deep learning. By automating emotion detection and feedback, this innovation addresses challenges in understanding emotions, enhancing decision-
making, and improving digital interactions across industries.By automating emotion detection and feedback, this innovation addresses challenges in understanding emotions, enhancing decision-making, and improving digital interactions across industries. For instance, it can be used to identify customer satisfaction or dissatisfaction in real time, which can help businesses to improve their products and services.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Flow Diagram for working model of Emotion Recognition from Text
Figure-2: Architecture for Emotion Recognition from Text and Feedback Analysis using DeepLearning.
Detailed Description of the Invention
The invention focuses on an innovative system and method that leverages advanced deep learning techniques to accurately detect emotions within textual content and generate insightful responses based on these emotions. In a world where digital communication is becoming increasingly prevalent, understanding the emotions conveyed in text is crucial for effective human-computer interaction, customer service, education, mental health support, and various applications.
In the initial stages, the model is trained using a data set to recognize and predict the emotions from the text. The data set contains various reviews or feedback given by the customers along with the emotions conveyed with the reviews which is very helpful. Customer feedback along with name, age, phone number, email and location are taken as input through a web interface. Then it analyses the emotion hidden in the given feedback. In the backend all the personal details of the customer are stored in the database. After careful inspection of the emotion present in the review or feedback, our invention then stores the emotion in the database along with generating an automated email tailored to the emotion it has analysed. If the feedback is happy, the reply will be enthusiastic. Whereas if the feedback is negative, the reply will be apologetic and constructive. In all cases, the system will also send an alert message to the concerned employee so that they can take appropriate action. This is about our invention from the customer point of view.
The invention operates through a series of interconnected stages:
a. Input Text Data: The process begins with the input of raw textual content, which can be sourced from diverse ways such as customer reviews, emails,and more. But customer review or feedback is particularly used in our invention.
b. Preprocessing: The raw text undergoes preprocessing, which includes tokenization to split the text into individual words or phrases, removal of punctuation, and handling of special characters and stop words. This stage aims to clean and structure the text for further analysis.
c. Text Embedding: After preprocessing, the textual data is transformed into numerical vectors using text embedding techniques. These embeddings capture the semantic meaning of words and phrases, allowing the deep learning model to process the text effectively.
d. Deep Learning Model: The core of the system involves a deep learning architecture, comprising multiple layers of interconnected neurons. This architecture could involve recurrent neural networks (RNNs) or transformer-based models like BERT or GPT.
e. Emotion Classification: The deep learning model’s primary task is to classify the embedded text into different emotion categories. These categories would include happiness, sadness, anger, surprise, and more. The model learns from labelled training data and generalizes its understanding to new, unseen text.
f. Detected Emotion: The output of the emotion classification stage is the detected emotion label associated with the input text. This label represents the primary emotion conveyed within the text.
g. Response Generation: Based on the detected emotion, the system employs predefined templates or rules to generate feedback. These templates are designed to provide relevant, context-aware responses or suggestions aligned with the detected emotion.
h. Generated Response: The generated response is in the form of text that is tailored to the detected emotion. For instance, if the detected emotion is "joy", the reply might be positive and encouraging, while reply for "sadness" or “anger” could be apologetic and supportive.
i. Output: The final output of the system is the generated response presented to the user. This feedback can be utilized in various contexts, such as responding to customer reviews, providing guidance in educational settings, offering emotional support, and enhancing content creation strategies. But in our invention, we are mainly focusing on responding to the customer reviews. Along with the response provided to the user the customer executive is also notified regarding the customer's dissatisfaction.
Hand in hand we have added the essence of data analytics in our invention. Our project provides the analytics of their product or service to the owner by considering the age and location as primary parameters. Our invention collects the stored data from the database and provides visual insights to the owner to understand their users emotions related to their products.We are segregating the users on two parameters one is the age, where users are then subdivided based on the specific age groups and provides the visual representations. The next segregation would be based on location, which provides the number of unhappy customers of each country. This allows the owner to make more area and age-oriented decisions to satisfy their customer expectations.
Advantages of the proposed model,
The proposed model of "Emotion Recognition from Text and Feedback Analysis using Deep Learning" offers several advantages, making it a promising solution for many domains and applications. Some of the key advantages include:
Higher Accuracy and Nuance: The deep learning model captures intricate patterns in text, leading to precise emotion detection that outperforms traditional methods.
Context-Aware Feedback: Generated feedback aligns with detected emotions, enhancing communication and interaction relevance.
Adaptability: The model learns from diverse data, improving accuracy and adapting to evolving linguistic trends.
Complexity Handling: Itinterprets subtle and complex emotions often missed by conventional techniques.
Reduced Manual Intervention: Automation saves time and resources, making the process more efficient.
Wide Applicability: Useful in customer service, education, mental health support, and market research, adaptable to specific needs.
Real-Time Responses: Automated nature enables swift reactions in real-time scenarios.
Consistency: Provides standardized feedback, reducing variations in human responses.
Data-DrivenInsights: Feedback offers insights into emotional trends, aiding in more effective decision-making.
Improvement Over Time: Continuous exposure to data enhances accuracy and performance.
Scalability: Efficiently processes large volumes of text data, suitable for various sources and applications.
Personalization: Can adapt to emotion of individual feedback there by offering personalized responses. , Claims:The scope of the invention is defined by the following claims:
Claims:
1. A system for emotion recognition from text and feedback analysis, comprising:
a) The Input module configured to receive textual content and a preprocessing module configured to process said textual content by tokenization and removal of punctuation and stop words
b) The text embedding module configured to convert processed textual content into numerical vectors using text embedding techniques.
c) The deep learning model comprising multiple neural network layers, configured to classify said numerical vectors into one or more emotion categories. The response generation module configured to generate contextually relevant response based on detected emotion categories
d) The output module configured to present generated response to the user and the data analytics module configured to help the organization to improve the customer satisfaction
2. According to claim 1, wherein the deep learning model employs recurrent neural networks (RNNs) to capture sequential relationships in the textual content.
3. According to claim 1,, wherein the text embedding module utilizes word embeddings to represent words in the textual content as continuous-valued vectors so that the deep learning model can better understand the numerical values.
4. According to claim 1, wherein generating response involves generation of reply aligned with the detected emotion. The detailed explanation involves generating the reply inclined towards the recognised emotion of the user feedback.
5. According to claim 1, wherein analysing the customer feedback involves creating the graphical representations making the organization harness the power of visual representation of data to take more effective steps.
6. According to claim 1, The additional feature of an alerting mechanism notifying concerned employees based on detected emotions. This is important because it allows the organization to quickly respond to customer problems and take action to resolve any issues.