Recognizing request and non-request messages in social networks with combined approaches

The aim of the request recognition task in social networks is to understand the intent behind the posts, comments, or messages shared by users.Many businesses are actively present on various social networks, making it crucial to identify user needs for marketers in this space to foster the growth of online businesses and e-commerce.Detecting request messages automatically and filtering them is essential.However, social network messages often contain slang and numerous spelling errors, posing challenges for research in this domain.While extensive research has been conducted in English, studies on this task in Persian are limited.Telegram stands out as the most popular social network in Iran, with a large Persian-speaking user base.This study utilized a standard labeled Persian dataset from Telegram for training and testing purposes, comprising 85741 messages from the platform, evenly split between request and non-request categories.To tackle the significant challenges posed by sarcastic messages and spelling mistakes on social media platforms, we devised a multi-step hybrid strategy.The initial step involves preprocessing.Social media data typically consists of unstructured and slangridden user messages, necessitating preprocessing to enhance Persian text processing and reduce slang usage.The pre-processing phase is crucial when dealing with social media platforms.Because Telegram is unique compared to other platforms the data cleaning process varies.This study's accomplishment includes developing a unique dataset and filtering out noise from Telegram enhancing improvement in the pre-processing phase.Also, this involves normalizing different word forms, such as "beautiful" and "beauty," to maintain the integrity of word meanings.The subsequent step focuses on feature extraction.Various approaches to feature extraction come with their own set of advantages and drawbacks.Hence, we employed hybrid feature extraction methods to address this complexity.While Tf-Idf methods assess word importance without considering meaning, FastText retains semantic similarity.By combining the bag of words and FastText methods, our research aims to enhance accuracy.The final step involves classification, where deep learning networks are utilized to evaluate these features.Experimental findings indicate that our final model achieves precision, recall, and f-score rates of nearly 90%, representing a 5% improvement on average compared to previous methodologies.