Visual Emotion Analysis via Affective Semantic Concept Discovery

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.

• TF-IDF, term frequency–inverse document frequency; Jieba, a Python package for word segmentation in the Chinese language.
• NLP is a significantly 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.
• Semantic

  and sentiment analysis should ideally combine to produce the most desired outcome.

• And if we want to know the relationship of or between sentences, we train a neural network to make those decisions for us.
• For example, there are an infinite number of different ways to arrange words in a sentence.
• Many recently proposed algorithms’ enhancements and various SA applications are investigated and presented briefly in this survey.

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 [22].

Translations for semantic analysis

Therefore, the reader can miss in this systematic mapping report some previously known studies. It is not our objective to present a detailed survey of every specific topic, method, or text mining task. This systematic mapping is a starting point, and surveys with a narrower focus should be conducted for reviewing the literature of specific subjects, according to one’s interests. Beyond latent semantics, the use of concepts or topics found in the documents is also a common approach. The concept-based semantic exploitation is normally based on external knowledge sources (as discussed in the “External knowledge sources” section) [74, 124–128].

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.

Critical elements of semantic analysis

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. [21]. We adopt the simplified version to extract 27-dimensional features and utilize the LibSVM classifier for image emotion classification.

Additionally, the output of such models is a number implying how similar the text is to the positive examples we provided during the training and does not consider nuances such as sentiment complexity of the text. One problem a sentiment analysis system has to face is contrastive conjunctions — they happen when one piece of writing consists of two contradictory words . What’s more, the usage of multilingual PLM allows us to perform sentiment analysis in over 100 languages of the world! Recently we contributed the science with our work about multilingual sentiment analysis, which was presented at one of the most notable and prestigious scientific conferences.

Crisis Communication

In the Options tab, set the number of topics to 30 in order to show as many subjects as possible for this set of documents but also to obtain a suitable explained variance on the computed truncated matrix. These two sentences mean the metadialog.com exact same thing and the use of the word is identical. Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs. It is a complex system, although little children can learn it pretty quickly.

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.

Sentilo: Semantic Web-based Sentiment Analysis

Semantic analysis allows you to cluster different data elements based on similarity, rather than preset classifications such as positive, negative and neutral. This helps you uncover important information like what exactly people are saying about your product or service; where and how they use it; and enhancements or new offerings they’re interested in. This type of valuable information can drive product development, new revenue streams and strategies for marketing, advertising and media planning. The use of Wikipedia is followed by the use of the Chinese-English knowledge database HowNet [82]. Finding HowNet as one of the most used external knowledge source it is not surprising, since Chinese is one of the most cited languages in the studies selected in this mapping (see the “Languages” section). As well as WordNet, HowNet is usually used for feature expansion [83–85] and computing semantic similarity [86–88].

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.

Table of Contents

For all its retrieved images of each concept, we adopt the pretrained AlexNet [34] 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.

Grobelnik [14] 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.

What Is Semantic Scholar?

This is particularly important for tasks such as sentiment analysis, which involves the classification of text data into positive, negative, or neutral categories. Without semantic analysis, computers would not be able to distinguish between different meanings of the same word or interpret sarcasm and irony, leading to inaccurate results. We adopt Flickr API provided by social media site Flickr to crawl user-generated tags in the affective dataset. With the aim of ensuring the correctness of concept discovery, we select meaningful semantic tags as candidate concepts of emotional semantics. Due to the existence of irregularities in user-defined tags, preprocessing are operated on the raw user-generated tags, including stop words removal, non-English words removal, and lemmatization.

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.

Sentiment Analysis Meets Semantic Analysis: Constructing Insight Knowledge Bases

However, the proposed solutions are normally developed for a specific domain or are language dependent. Jovanovic et al. [22] discuss the task of semantic tagging in their paper directed at IT practitioners. Semantic tagging can be seen as an expansion of named entity recognition task, in which the entities are identified, disambiguated, and linked to a real-world entity, normally using a ontology or knowledge base. The authors compare 12 semantic tagging tools and present some characteristics that should be considered when choosing such type of tools.

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,[79] 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.

• Hence, we replaced those and user-added emoticons and symbols with emotion-colored textual statements to neutralize the non-textual data as much as possible.
• Hence, it is critical to identify which meaning suits the word depending on its usage.
• Semantic network of user reviews after the second apology statement by NetEase.
• The purple network (28%) describes how players felt about the “Treasure system,” with the keywords “game cards” and “balance” referring to the game’s new version disrupting the game’s content and balance, respectively.
• Published in 2013 by Mikolov et al., the introduction of word embedding was a game-changer advancement in NLP.
• As shown in Table 6, the emotional scores of the first apology were 46.61, 13.89, 14.67, 08.52, and 16.31%, respectively.