In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

Humanoid robots with artificial intelligence is very captivating field for people, since the robots are introduced. For humanoid robots we can introduce new ideas without any limitations and constraints but in reality there are limitations to implement them. In this paper we are discussing different humanoid robots with their practical applications. Based on their implementation structure detailed comparison of their characteristics is performed and on this bases some limitations and future work has been discussed.

Sentiment analysis is the field that analyzes sentiments, and opinions of people about entities such as products, businesses, and events. As opinions influence the people’s behaviors, it has numerous applications in real life such as marketing, politics, social media etc. Stance detection is the sub-field of sentiment analysis. The stance classification aims to automatically identify from the source text, whether the source is in favor, neutral, or opposed to the target. This research study proposed a framework to explore the performance of the conventional (NB, DT, SVM), ensemble learning (RF, AdaBoost) and deep learning-based (DBN, CNN-LSTM, and RNN) machine learning techniques. The proposed method is feature centric and extracted the (sentiment, content, tweet specific and part-of-speech) features from both datasets of SemEval2016 and SemEval2017. The proposed study has also explored the role of deep features such as GloVe and Word2Vec for stance classification which has not received attention yet for stance detection. Some base line features such as Bag of words, N-gram, TF-IDF are also extracted from both datasets to compare the proposed features along with deep features. The proposed features are ranked using feature ranking methods such as (information gain, gain ration and relief-f). Further, the results are evaluated using standard performance evaluation measures for stance classification with existing studies. The calculated results show that the proposed feature sets including sentiment, (part-of-speech, content, and tweet specific) are helpful for stance classification when applied with SVM and GloVe a deep feature has given the best results when applied with deep learning method RNN.

Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection.

Software quality is specific property which tells what kind of standard software should have. In a software project, quality is the key factor of success and decline of software related organization. Many researches have been done regarding software quality. Software related organization follows standards introduced by Capability Maturity Model Integration (CMMI) to achieve good quality software. Quality is divided into three main layers which are Software Quality Assurance (SQA), Software Quality Plan (SQP) and Software Quality Control (SQC). So In this study, we are discussing the quality standards and principles of software projects in Pakistan software Industry and how these implemented quality standards are measured and managed. In this study, we will see how many software firms are following the rules of CMMI to create software. How many are reaching international standards and how many firms are measuring the quality of their projects. The results show some of the companies are using software quality assurance techniques in Pakstan.