###### Abstract
[7] in 2001, and the model worked on a feed-forward neural network that had been trained using 14 million words. The reason they are rarely used is that these early embedding models outperform conventional models with hand-crafted variables. A range of NLP tasks quickly gained popularity for a collection of word2 vec models [8] that Google released in 2013, which were trained on 6 billion words. Using Google's Transformer [9], a fresh NN architecture, in 2018, embedding models were produced by OpenAI. For text-generating projects, their original model, GPT [10], is now extensively used. The same year, Google created BERT [7], a bidirectional Transformer-based system. BERT, which includes 340 million parameters and 3.3 billion words of training data, is currently the most advanced embedding model. It is possible for convolutional neural networks (CNN) [8] to learn local responses from spatial or temporal data, but not sequential correlations. Short-term dependencies in a sequence of data can be handled by recurrent neural networks (RNNs) [9], but long-term relationships are a problem for these networks. To overcome the constraints of the existing systems in evaluating user sentiments for a certain service or product, a unique methodology based on deep learning utilizing XLNet has been developed. The existing sentiment categorization systems have two issues with handling missing context semantics in text:  
1. The existing studies primarily use language symbol information in texts to classify sentiments. Only a few research have looked at sentiment data with punctuation marks in the dataset. The issue of text context semantics can be resolved with the aid of punctuation symbols that include sentimental information;2. The majority of the ongoing research is focused on the extraction of emotional characterizations and the modeling of textual material at the document level. On the other hand, studies rarely take into consideration doing other levels of text content, such as words or phrases. To overcome the lack of text context semantics in social media assessments, sentiment information can be efficiently collected from many levels via the extraction of sentiment features and by modeling texts from various levels. Given the above issues in existing models for sentiment classification, a model named the manifold and multi-level sentiment modeling method (MFMLSC) is proposed. Therefore, the main contributions of this work are as follows:  
1. Based on two dimensions, language symbols and emoticon symbols, the manifold sentiment classification method (MFSC) is proposed. In this approach, the problem of text context semantics missing in text reviews is tackled using the word, sentence, and document levels;
2. The multi-dimensional sentiment classification method (MDSC) uses two symbol types, i.e., emoticons symbols and linguistic symbols. This approach is used to tackle the problem of missing context information from texts, which plays a significant role in obtaining hidden information from sentiments;
3. Based on the effectiveness of these two models, the final model is proposed as the multi-fold and multi-level sentiment modeling method (MFMLSC)
4. The proposed model is implemented on three different datasets of Google Pay, Phonpe, and Paytm mobile app reviews. Additionally, the proposed model is validated on the IMDB benchmark dataset. The rest of the sections are organized as follows. Section 2 discusses the related work. Section 3 provides details and describes the workings of the proposed model. In Section 4, various settings and evaluation parameters are discussed. In Section 5, a summary and the conclusions are presented.