Words are not always used in their original form when it comes to expressions for example (it’s not pie, rain check…). Internet users don’t hesitate to modify the structure of sentences (absence of verbs, incomplete sentences) and sometimes reproduce in writing certain characteristics related to speaking. NLP is a significantly metadialog.com helpful field of computer science and AI that mainly focuses on the interaction among humans and computers, making it easier to analyze and process textual data. As more effort is made into designing more advanced algorithms, we can expect to see machines become more accurate at recognizing and understanding the human language.
Grobelnik  also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness. The most simple level is the lexical level, which includes the common bag-of-words and n-grams representations. The next level is the syntactic level, that includes representations based on word co-location or part-of-speech tags.
However, traditional statistical methods often fail to capture the richness and complexity of human language, which is why semantic analysis is becoming increasingly important in the field of data science. Because of the casualness of user-defined tags, some users tend to describe one image with multiple tags in similar semantics to facilitate photo sharing and retrieval. In our work, we refer to this property of emotion-related concept set as informativeness modelled by a strategy like mutual information. The formula iswhere and are the probability of the th and th concept appearing in the dataset, respectively.
For all its retrieved images of each concept, we adopt the pretrained AlexNet  model to extract the image features. We extract the CNN features on each image and feed them into the linear classifiers to generate the concept scores. Assuming the feature vector of each image is denoted as , where is the overall number of concepts, is the score produced for the concept classifier and the feature vector is a series of all concept classifier scores produced on the image . On a daily basis, opinions influence our daily behaviors and are at the core of almost all human activities. Opinions and their related concepts, such as sentiments, attitudes and emotions, are also the focus of sentiment analysis and opinion mining, which are data analytics processes that can assess and label the sentiments within textual data.
We start our report presenting, in the “Surveys” section, a discussion about the eighteen secondary studies (surveys and reviews) that were identified in the systematic mapping. In the “Systematic mapping summary and future trends” section, we present a consolidation of our results and point some gaps of both primary and secondary studies. We compare our method against several baselines, including methods using low-level features, midlevel semantic features as well as high-level concept features. For the methods based on low-level features, we compare with the principle-of-art features (PAEF) designed by Zhao et al. . We adopt the simplified version to extract 27-dimensional features and utilize the LibSVM classifier for image emotion classification.
A modularity score close to zero indicates that the fraction of edges among communities is no better than random and that close to one indicates that the network community structure is as strong as it can be (Chen et al., 2014). We constructed an undirected graph to study the high TF-IDF words in gamer comments. The nodes in the graphs represent common TF-IDF keywords, and the edges indicate the co-occurrence relationship between two nodes. Thus, the nodes’ size reflects the TF-IDF size, and the thickness of the edges reflects the frequency with which the two nodes appear together.
The actual sentiment labels of reviews are shown by green (positive) and red (negative). It is evident from the plot that most mislabeling happens close to the decision boundary as expected. Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans.
IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data. It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text. According to IBM, semantic analysis has saved 50% of the company’s time on the information gathering process. Semantic analysis refers to a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data.
With fast growing world there is lot of scope in the various fields where uncertainty play major role in deciding the probability of uncertain event. Hence, it is required to use different techniques for the extraction of important information on the basis of uncertainty of verbs and highlight the sentence. 9, we can observe the predominance of traditional machine learning algorithms, such as Support Vector Machines (SVM), Naive Bayes, K-means, and k-Nearest Neighbors (KNN), in addition to artificial neural networks and genetic algorithms.
The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions. Powerful machine learning tools that use semantics will give users valuable insights that will help them make better decisions and have a better experience. If combined with machine learning, semantic analysis lets you dig deeper into your data by making it possible for machines to pull purpose from an unstructured text at scale and in real time. Researchers also found that long and short forms of user-generated text should be treated differently. An interesting result shows that short-form reviews are sometimes more helpful than long-form, because it is easier to filter out the noise in a short-form text. For the long-form text, the growing length of the text does not always bring a proportionate increase in the number of features or sentiments in the text.
The experimental results are promising in terms of Precision, Recall, and F-measure. Early studies on this issue explored handcrafted features inspired by artistic or psychology theories, including color, texture, SIFT-based shape descriptors, composition and symmetry [6, 20, 21]. However, the handcrafted features are unable to solve the problem of the semantic gap well, as they are most effective on small-scale datasets containing specific styles of images, like artistic images. Recently, deep learning-based features have been widely adopted in image emotion recognition extracting more discriminative features .
Consumers are always looking for authenticity in product reviews and that’s why user-generated videos get 10 times more views than brand content. This study combines SNA and sentiment analysis to measure whether the enterprise’s crisis communication strategy has the expected impact on users’ attitudes. First, SNA was used to conduct a content analysis on comments; then, sentiment analysis was used to calculate the emotional polarity of the comments. For example, suppose the proportion of negative emotions in the comments was much higher than positive ones. In this case, the high-frequency words and cluster analyses generated by SNA would not be positive.
Finally, SALOM can deal with different aspects exist in the same review sentence. The nearest aspects’ synonyms and related words extraction step is applied to each exact aspect. Therefore, for each exact product aspect firstly, its synonyms, hyponyms, and hypernyms are extracted using Wordnet glossary.
The review reported in this paper is the result of a systematic mapping study, which is a particular type of systematic literature review [3, 4]. Systematic literature review is a formal literature review adopted to identify, evaluate, and synthesize evidences of empirical results in order to answer a research question. It is extensively applied in medicine, as part of the evidence-based medicine . This type of literature review is not as disseminated in the computer science field as it is in the medicine and health care fields1, although computer science researches can also take advantage of this type of review. We can find important reports on the use of systematic reviews specially in the software engineering community [3, 4, 6, 7].
Besides, the affective concept set contains fewer concepts with high semantic similarity, which ensures the diversity of the concept set and avoids the repetition of redundant information. These results demonstrate the availability of the proposed concepts selection model for affective semantic concepts discovery. As described earlier, there is a correlation between the visual concept and emotion conveyance.
Employee, Applicant, and Customer are generalized into one object called Person. The object Person is related to the object's Project and Task. A Person owns various projects and a specific task relates to different projects. This example can easily assign relations between two objects as semantic data.