Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which performs better for Roman Urdu Sentiment analysis. Our suggested model employs the BiLSTM network to maintain the context in both directions and eventually, results for ternary classification are obtained by using the final softmax output layer. A manually labeled data set was used to evaluate the model, data is collected from the HEIs of Pakistan. Model was empirically evaluated on two datasets of Roman Urdu, the newly developed student’s feedback dataset and RUSA-19 publically available data set of Roman Urdu. Our model performs effectively using the word embedding and BiLSTM layer. The proposed model is compared with the baseline models of CNN, RNN, GRU and classic LSTM. The experimental findings demonstrate the proposed model's efficacy with an F1score of 90%.
