DEEP LEARNING APPROACH TO SENTIMENT ANALYSIS IN SOCIAL MEDIA

Abstract

The present invention provides a system and method for performing sentiment analysis on social media data using deep learning techniques, such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, and transformer-based models. By leveraging advanced natural language processing (NLP) methods, the system accurately classifies social media posts into sentiment categories, including positive, negative, and neutral, while handling the complexities of informal language, slang, emojis, and context-specific expressions. The system is capable of real-time or near-real-time processing, providing valuable insights into public sentiment for applications such as brand monitoring, market analysis, and social listening.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention pertains to a system and method for performing sentiment analysis on social media data using deep learning models. This approach leverages advanced machine learning techniques, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer-based models, to process and classify textual data extracted from social media platforms. The system is designed to handle the inherent complexities of social media data, including informal language, slang, emojis, and context-specific nuances.

The system collects social media posts from multiple platforms like Twitter, Facebook, Instagram, and others. The data collection process involves querying social media APIs or scraping content from these platforms. The posts, comments, and hashtags are then aggregated into a dataset for analysis. The collected data may include metadata such as user information, timestamps, geographical location, and post frequency, which can be valuable for deeper sentiment analysis and trend identification.

Raw social media data typically contains a significant amount of noise, including misspellings, slang, abbreviations, emojis, and hashtags, which can hinder accurate sentiment analysis. To address this, the system performs data preprocessing steps such as tokenization, stop-word removal, and stemming. Additionally, specialized techniques are employed to handle informal language, such as mapping slang to its standard form or interpreting emojis and hashtags as part of the sentiment context. This preprocessing ensures that the input text is properly formatted for deep learning models.

The preprocessed data is used to train a deep learning model. In one embodiment, an RNN or LSTM is employed due to its ability to process sequential data and capture contextual relationships between words. For more complex analysis, a transformer-based model, such as BERT (Bidirectional Encoder Representations from Transformers), may be used to better understand the context and dependencies within the data. The model is trained using labeled sentiment datasets, where the text is categorized into predefined sentiment classes such as positive, negative, neutral, or more specific emotions like anger, joy, or surprise.

One of the key innovations of the present invention is the contextual understanding that the deep learning models are capable of. Social media posts often contain ambiguity, sarcasm, or irony that makes sentiment classification difficult. The deep learning models are designed to account for these nuances by learning the relationships between words and phrases in context. For example, the model might recognize that a sarcastic comment, such as "I absolutely love waiting in long lines at the store," should be classified as negative sentiment despite the use of positive words like "love."

The system is capable of performing real-time or near-real-time sentiment analysis. This is particularly important for applications such as brand monitoring, crisis management, and social listening, where insights need to be generated promptly. As new social media posts are collected, the system processes them through the trained deep learning models and classifies the sentiment accordingly. Results can be made available through an API or displayed via a user-friendly interface.

After analyzing the sentiment of social media posts, the system generates visualizations that allow users to track sentiment trends over time. These visualizations can include sentiment distribution graphs, heat maps, or word clouds that highlight common terms associated with positive or negative sentiments. Furthermore, actionable insights, such as spikes in negative sentiment related to a product or event, can be automatically flagged and reported to the user.

In first embodiment, the invention is implemented as a social media monitoring system that continuously collects posts from Twitter, Facebook, and Instagram in real-time. The system uses deep learning models to analyze the sentiment of these posts with respect to a specific brand or product. Preprocessing steps include removing noise such as stop words and correcting common misspellings, while the sentiment analysis model identifies whether the posts express positive, negative, or neutral opinions. The system provides insights into user sentiment trends, allowing the brand's marketing team to identify areas for improvement and respond to customer feedback in real-time.

In another embodiment, the invention is applied to analyze public sentiment towards political events, candidates, or policies. The system collects tweets and posts from various social media platforms that include hashtags related to a specific political issue. After preprocessing the data to address slang and political jargon, the deep learning model classifies the sentiment of each post. The system can then generate sentiment reports, showing how public opinion shifts over time regarding a particular political subject. Additionally, the tool identifies emerging trends or issues that may require immediate attention from political campaign teams or policymakers.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for performing sentiment analysis on social media data, comprising the steps of:
collecting a set of social media posts from one or more social media platforms;
preprocessing the collected posts by performing tokenization, stop-word removal, and stemming;
training a deep learning model on the preprocessed data, wherein the model is a recurrent neural network (RNN), long short-term memory (LSTM) network, or transformer model;
using the trained model to classify each social media post into a sentiment class, wherein the sentiment classes include positive, negative, or neutral.

2.The method of claim 1, wherein the deep learning model further comprises contextual sentiment analysis to handle ambiguous or sarcastic expressions in the social media posts.

3.The method of claim 1, wherein the preprocessing step further includes the handling of slang and informal language common in social media posts.

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