Text-Based Intent Analysis using Deep Learning

Abstract

Intent classification focuses on analyzing input to gauge the users’ needs and opinions. With this proposed system, we utilise a neural network classifier that analyzes text strings that are fed to it as input. Based on the analyzed intent, the system responds to the user by acting upon the commands issued to it. The areas covered by the system include weather, ratings, and booking restaurants. Firstly the intent of the text will be analysed and then it will be classified based on the topic it belongs to. Due to expressiveness of intent by users intent , for instance ”need a rental space”, ”places to visit in chennai” intent analysis plays a major role in ecommerce, real estate and online marketing industry. We aim to maximize the accuracy and minimize the errors and the time complextity of the proposed system

Text Classification on Customer Feedback: A Systematic Literatures Review

“…Dataset size [23], [25], [29], [31], [44], [45] <5,000 [20], [22], [26], [30], [43], [46] 5,000-20,000 [24], [36], [38], [47] 20,000-50,000 [21] 50,000-100,000 [33], [34], [35], [40] >100,000 [27], [28], [32], [37], [39], [41], [42] Not available…”

“…Performance metric [21], [25], [41], [47] Accuracy [28], [32] Accuracy, precision, recall [30], [44] Accuracy, F1-score [20], [23], [24], [26], [29], [31], [36], [37] Accuracy, precision, recall, F1-score [22], [27] Precision, recall, F1-score [38] F1-score [34] Perception score [46] Mapping quality characteristic [40] Relationship (online reviews-hotel sales) [43] Classify user component [33] Confusion matrix Consequently, it is deemed proficient at classifying forthcoming values. According to the data presented in Table 8, it can be observed that the random forest technique exhibits the highest level of accuracy among the ML algorithms, achieving a remarkable accuracy rate of 99.2% when applied to the TripAdvisor dataset.…”

A Comparative Study on Various ANN Optimization Algorithms for Magnetorheological Elastomer Carbonyl Iron Particle Concentration Estimation

“…iii. Stochastic Gradient Descent (SGD) SGD is considered as a good learning algorithm for large training data set to train the neural network where the new updated parameter involved with a single or a few parameters such as learning rate to reduce variance and lead to stable convergence [32].…”

“…AdaGrad adapt all model parameters by scaling them inversely proportional to the square root of the sum of all the historical squared values of gradient [32]. In addition, a high gradient for the parameters will have a reduced learning rate and parameters with small gradient will have increase in learning rate [31].…”