In the field of human-computer interaction, identifying emotion from speech and understanding the full context of spoken communication is a challenging task due to the imprecise nature of emotion, which requires detailed speech analysis. In the area of speech emotion recognition, various techniques have been employed to extract emotions from audio signals, including several well-established speech analysis and classification methods. Despite numerous advancements in recent years, many studies still fail to consider the semantic information present in speech. Our study proposes a novel approach that captures both the paralinguistic and semantic aspects of the speech signal by combining state-of-the-art machine learning techniques with carefully crafted feature extraction strategies. We address this task using feature-engineering-based techniques, which involve extracting meaningful audio features such as energy, pitch, harmonics, pauses, central momentum, chroma, zero-crossing rate, and Mel-frequency cepstral coefficients (MFCCs). These features capture important acoustic patterns that help the model learn emotional cues more effectively. This work is primarily conducted on the IEMOCAP dataset, a large and well-annotated emotional speech corpus. By framing our task as a multi-class classification problem, we extract 15 features from the audio signal and use them to train five machine learning classifiers. Additionally, we incorporate text-domain features to reduce ambiguity in emotional interpretation. We evaluate our model's performance using accuracy, precision, recall, and F-score across all experiments.

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.