How to Conduct a Social Media Sentiment Analysis

This concept is further supported by the fact that using machine translation and sentiment analysis models trained in English, we achieved high accuracy in predicting the sentiment of non-English languages such as Arabic, Chinese, French, and Italian. The obtained results demonstrate that both the translator and the sentiment analyzer models significantly impact the overall performance of the sentiment analysis task. It opens up new possibilities for sentiment analysis applications in various fields, including marketing, politics, and ChatGPT App social media analysis. Some methods combining several neural networks for mental illness detection have been used. For examples, the hybrid frameworks of CNN and LSTM models156,157,158,159,160 are able to obtain both local features and long-dependency features, which outperform the individual CNN or LSTM classifiers used individually. Sawhney et al. proposed STATENet161, a time-aware model, which contains an individual tweet transformer and a Plutchik-based emotion162 transformer to jointly learn the linguistic and emotional patterns.

• The internet is full of information and sources of knowledge that may confuse readers and cause them to spend additional time and effort in finding relevant information about specific topics of interest.
• Because depression is related to reduced self-agency, it is also possible that self-referential processing will actually be a correlate of reduced self-agency.
• That is, the average news sentiment prevailing over weekend will be applied to the following Monday.
• Deep learning enhances the complexity of models by transferring data using multiple functions, allowing hierarchical representation through multiple levels of abstraction22.

Content features were extracted using Term Frequency/Inverse Document Frequency (TFIDF) to identify significant terms in each post. Sentiment features were derived from the grouping of second-person pronouns such as ‘you’, which could be used to form a harassment format. Contextual features were also included to distinguish between posts that had a harassment-like quality. The similarity of these features was then computed to detect potential cases of online harassment.

Ablation study

The value of k is usually set to a small number to ensure the accuracy of extracted relations. You can foun additiona information about ai customer service and artificial intelligence and NLP. Furthermore, we use a threshold (e.g., 0.001 in our experiments) to filter out the nearest neighbors not close enough in the embedding space. Our experiments have demonstrated that the performance of supervised GML is robust w.r.t the value of k provided that it is set within a reasonable range (between 1 and 9). In GML, features serve as the medium for knowledge conveyance between labeled and unlabeled instances. A wide variety of features usually need to be extracted to capture diverse information. For each type of feature, this step also needs to model its influence over label status.

This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics. Following this, the relationship between words in a sentence is examined to provide clear understanding of the context.

In “Experimental testing” section the model is approbated in its ability to simulate human judgment of semantic connection between words of natural language. Positive results obtained on a limited corpus of documents indicate potential of the developed theory for semantic analysis of natural language. Interested in natural language processing, machine learning, cultural analytics, and digital humanities. Published in 2013 by Mikolov et al., the introduction of word embedding was a game-changer advancement in NLP.

Table 11 show that the model gets confused when it found comments that have sarcasm, figurative speech, or sentiment sentences that contain both words that give positive and negative sentiment in one comment. For example,

and the first sentence contains the positive words like while the second sentience contain . The word implies a positive sentiment while the overall sentiment of the comment is negative caused the model to predict the sentiment as positive.

• In other words, sentiment analysis turns unstructured data into meaningful insights around positive, negative, or neutral customer emotions.
• In their news coverage of the COVID-19 pandemic, the news discourse of the mainstream US media also showed a clear tendency to depict China as a cultural or racial “other” (Chung et al. 2021).
• Also, Gensim includes several kinds of algorithms such as LDA, RP, LSA, TF-IDF, hierarchical Dirichlet processes (HDPs), LSI, and singular value decomposition (SVD).
• The newspaper employed the strategy of predication to describe “renminbi”, and its positive attitude toward its surge in value is strengthened by “-ed” phrase following it.
• Following model construction, hyperparameters were fine-tuned using GridSearchCV.

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon.

While these results mark a significant milestone, challenges persist, such as the need for a more extensive and diverse dataset and the identification of nuanced sentiments like sarcasm and figurative speech. The study underscores the importance of transitioning from binary sentiment analysis to a multi-class classification approach, enabling a finer-grained understanding of sentiments. Moreover, the establishment of a standardized corpus for Amharic sentiment analysis emerges as a critical endeavor with broad applicability beyond politics, spanning domains like agriculture, industry, tourism, sports, entertainment, and satisfaction analysis.

This model effectively handles multiple sentiments within a single context and dynamically adapts to various ABSA sub-tasks, improving both theoretical and practical applications of sentiment analysis. This not only overcomes the simplifications seen in prior models but also broadens ABSA’s applicability to diverse real-world datasets, setting new standards for accuracy and adaptability in the field. In our approach to ABSA, we introduce an advanced model that incorporates a biaffine attention mechanism to determine the relationship probabilities among words within sentences. This mechanism generates a multi-dimensional vector where each dimension corresponds to a specific type of relationship, effectively forming a relation adjacency tensor for the sentence. To accurately capture the intricate connections within the text, our model converts sentences into a multi-channel graph.

Overall, this study offers valuable insights into the potential of semantic network analysis in economic research and underscores the need for a multidimensional approach to economic analysis. This study contributes to consumer confidence and news literature by illustrating the benefits of adopting a big data approach to describe current economic conditions and better predict a household’s future economic activity. The methodology in this article uses a new indicator of semantic importance applied to economic-related keywords, which promises to offer a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods. The potential benefits of utilizing text mining of online news for market prediction are undeniable, and further research and development in this area will undoubtedly yield exciting results. For example, future studies could consider exploring other characteristics of news and textual variables connected to psychological aspects of natural language use73 or consider measures such as language concreteness74.

Dataset

A natural language processing (NLP) technique, sentiment analysis can be used to determine whether data is positive, negative, or neutral. Besides focusing on the polarity of a text, it can also detect specific feelings and emotions, such as angry, happy, and sad. Sentiment analysis is even used to determine intentions, such as if someone is interested or not. Sentiment lexicon-based approaches rely too much on the quality and coverage of the sentiment lexicon, with limited scalability and objectivity. The meanings of sentiment words may vary with context and time, increasing the limitations of the lexicon26; In addition, the development of sentiment lexicons and judgment rules requires a great deal of manual design and priori knowledge. The difficulties of sentiment annotation make the quality of the lexicons uneven.

By analyzing likes, comments, shares and mentions, brands can gain valuable insights into the emotional drivers that influence purchase decisions as well as brand loyalty. This helps ‌tailor marketing strategies, improve customer service and make better business decisions. With markets increasingly competitive and globalized, staying on top of data is essential for understanding overall business performance and making informed decisions. “Integrating document clustering and topic modeling,” in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (Bellevue, WA), 694–703.

Since the predicted labels of \(t_2\) and \(t_3\) provide \(t_4\) labeling with correct polarity hints, \(t_4\) is also correctly labeled as positive. The accuracy of the LSTM based architectures versus the GRU based architectures is illastrated in Fig. Results show that GRUs are more powerful to disclose features from the rich hybrid dataset.

Recognizing Textual Entailment (RTE) is also an NLP task aimed at modelling language variability by identifying the textual entailment relationship between different words or phrases. Typically, RTE tasks involve two natural language expressions (mostly two sentences) that have a directional relationship. In these tasks, the entailing expression is referred to as the text (T), and the entailed expression is referred to as the hypothesis (H).

NLTK (Natural Language Toolkit)

The major difference between Arabic and English NLP is the pre-processing step. All the classifiers fitted gave impressive accuracy scores ranging from 84 to 85%. While Naive Bayes, logistic regression, and random forest gave 84% accuracy, an improvement of 1% was achieved with linear support vector machine. The models can be improved further by applying techniques such as word embedding and recurrent neural networks which I will try to implement in a follow-up article.

To do so, we have created our own Web data extraction and database solution, orchestrating existing software and implementing all needed connectors. The correlation coefficient r is equal to –0.45 and the p-value, in Figure 2 is below 0.05 so we can reject the null hypothesis and conclude that the relationship between negative sentiment captured from the headlines is moderate and statistically significant. The feedback can inform your approach, and the motivation and positive reinforcement from a great customer interaction can be just what a support agent needs to boost morale.

From the sexual harassment sentences, the types of sexual harassment are manually labelled. The first type of label is the sexual harassment type, it has labels which are gender semantic analysis of text harassment, unwanted sexual attention, and sexual coercion. The second type of label is the sexual offence type, which has labels that are physical and non-physical.

Affective computing and sentiment analysis21 can be exploited for affective tutoring and affective entertainment or for troll filtering and spam detection in online social communication. This work discusses about the way for the development of more bioinspired approaches to the design of intelligent sentiment-mining systems that can handle semantic knowledge, make analogies, learn new affective knowledge, and detect, perceive, and “feel” emotions. Sentiment analysis is performed on Tamil code-mixed data by capturing local and global features using machine learning, deep learning, transfer learning and hybrid models17. Out of all these models, hybrid deep learning model CNN + BiLSTM works well to perform sentiment analysis with an accuracy of 66%. In18, aspect based sentiment analysis known as SentiPrompt which utilizes sentiment knowledge enhanced prompts to tune the language model. This methodology is used for triplet extraction, pair extraction and aspect term extraction.

The neural network is trained to optimize for translation accuracy, considering both the meaning and context of the input text. One advantage of Google Translate NMT is its ability to handle complex sentence structures and subtle nuances in language. Based on the Natural Language Processing Innovation Map, the Tree Map below illustrates the impact of the Top 9 NLP Trends in 2023. Virtual assistants improve customer relationships and worker productivity through smarter assistance functions. Advances in learning models, such as reinforced and transfer learning, are reducing the time to train natural language processors.

The tool can handle 242 languages, offering detailed sentiment analysis for 218 of them. Monitor millions of conversations happening in your industry across multiple platforms. Sprout’s AI can detect sentiment in complex sentences and even emojis, giving you an accurate picture of how customers truly think and feel about specific topics or brands. Here are a couple examples of how a sentiment analysis model performed compared to a zero-shot model.

Finally, a review is defined as neutral with a polarity score of 0 if it contains the same number of negative and positive words. The essential component of any sentiment analysis solution is a computer-readable benchmark corpus of consumer reviews. One of the most significant roadblocks for Urdu SA is a lack of resources, such as the lack of a gold-standard dataset of Urdu reviews.

What is sentiment analysis?

Convolutional layers help capture more abstracted semantic features from the input text and reduce dimensionality. RNN layers capture the gesture of the sentence from the dependency and order of words. Sentiment analysis is as important for Urdu dialects as it is for any other dialect. Many obstacles make SA of the Urdu language difficult such as Urdu contains both formal and informal verb forms as well as masculine and feminine genders for each noun. Similarly, the Persian, Arabic, and Sanskrit languages have their terms in Urdu.

However, it is essential to note that this approach can be resource-intensive in terms of time and cost. Nevertheless, its adoption can yield heightened accuracy, especially in specific applications that require meticulous linguistic analysis. Moreover, the Proposed Ensemble model consistently delivered competitive results across multiple metrics, emphasizing its effectiveness as a sentiment analyzer across various translation contexts. This observation suggests that the ensemble approach can be valuable in achieving accurate sentiment predictions. A series of graphs have been generated to visually represent the experimental outcomes of various combinations of translators and sentiment analyzer models, offering a comprehensive insight into the effectiveness of these models in sentiment analysis shown in Fig.

There are usually multiple steps involved in cleaning and pre-processing textual data. I have covered text pre-processing in detail in Chapter 3 of ‘Text Analytics with Python’ (code is open-sourced). However, in this section, I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines ChatGPT and I frequently use them in my NLP projects. We will be leveraging a fair bit of nltk and spacy, both state-of-the-art libraries in NLP. However, in case you face issues with loading up spacy’s language models, feel free to follow the steps highlighted below to resolve this issue (I had faced this issue in one of my systems).

When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity. This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products. The CBOW model is trained by adjusting the weights of the embedding layer based on its ability to predict the target word accurately. The individual context word embeddings are aggregated, typically by summing or averaging them. The training process involves adjusting the parameters of the embedding model to minimize the difference between predicted and actual words in context. To accurately identify and classify entities (e.g., names of people, organizations, locations) in text, word embeddings help the model understand the context and relationships between words.

Sentiment Analysis

You can route tickets about negative sentiments to a relevant team member for more immediate, in-depth help. Because different audiences use different channels, conduct social media monitoring for each channel to drill down into each audience’s sentiment. For example, your audience on Instagram might include B2C customers, while your audience on LinkedIn might be mainly your staff. These audiences are vastly different and may have different sentiments about your company.

This indicates that topics extracted from news could be used as a signal to predict the direction of market volatility next day. The results obtained from our experiment are similar to those of Atkins et al. (2018) and Mahajan et al. (2008). The accuracy was slightly lower for the tweets dataset, which can be explained by the fact that tweets text typically contains abbreviations, emojis and grammatical errors which could make it harder to capture topics from tweets. First, we followed Kelechava’s methodology3 to convert topics into feature vectors. Then, an LDA model was used to get the distribution of 15 topics for every day’s headlines.

Finally, we evaluate the model and the overall success criteria with relevant stakeholders or customers, and deploy the final model for future usage. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech and so on. In this series of articles, we will be looking at tried and tested strategies, techniques and workflows which can be leveraged by practitioners and data scientists to extract useful insights from text data. This article will be all about processing and understanding text data with tutorials and hands-on examples. We must admit that sometimes our manual labelling is also not accurate enough.

Urdu is written from right to left, and the distinction between words is not always clear. The scarcity of acknowledged lexical resources24,25 and the lack of Urdu text data due to morphological concerns. Rather than a conventional text encoding scheme, most Urdu websites are organized in an illustrated manner, which complicates the task of producing a state-of-the-art machine-readable corpus. The well-known sentiment lexicon database is an essential component for constructing sentiment analysis classification applications in any dialect.

This section analyses the performance of proposed models in both sentiment analysis and offensive language identification system by examining actual class labels with predicted one. The first sentence is an example of a Positive class label in which the model gets predicted correctly. The same is followed for all the classes such as positive, negative, mixed feelings and unknown state.

In the above example, the verb in the source text is “been”, but the predicate is changed to the verb “下滑(decline)” in the translation, which comes from the word “slide” in the source text. Transformation in predicates of this kind, known as denominalization, is essentially one of the major factors contributing to the difference in semantic depths of verbs. Through denominalization in the translation process, the notion of “decline” is reintroduced to the predicate verb, which eliminates the incongruency between the lexico-grammatical and semantic layers, resulting in more explicit information. The danmaku texts contain internet popular neologisms, which need to be combined with the video content to analyze the potential meanings between the lines, and the emotion annotation is difficult. Currently, it is widely recognized that individuals produce emotions influenced by internal needs and external stimuli, and that when an individual’s needs are met, the individual produces positive emotions, otherwise negative emotions are generated38. Therefore, this paper decomposes and maps the hierarchy of needs contained in danmaku content, which can be combined with video content to make a more accurate judgment of danmaku emotions.