Sentiment Analysis with Deep Learning by Edwin Tan

We chose MonkeyLearn as one of the top sentiment analysis tools because it helps businesses access real-time analysis with easy integrations from third-party apps. This platform also enables users to trigger actions and set up rules based on sentiments, such as escalating negative cases, prioritizing positive comments, or tagging tickets. MonkeyLearn’s workflow integrations provide a holistic view of customer sentiments gathered from various sources, resulting in rich insights and more actionable data. After the data were preprocessed, it was ready to be used as input for the deep learning algorithms.

This coverage helps businesses understand overall market conversations and compare how their brand is doing alongside their competitors. Meltwater also provides in-depth analysis of various media, such as showing the overall tonality of any given article or mention, which gives you a holistic context of your brand or topic of interest. On October 7, Hamas launched a multipronged attack against Israel, targeting border villages and extending checkpoints around the Gaza Strip. The attack used armed rockets, expanded checkpoints, and helicopters to infiltrate towns and kidnap Israeli civilians, including children and the elderly1. Moreover, the Gaza conflict has led to widespread destruction and international debate, prompting sentiment analysis to extract information from users’ thoughts on social media, blogs, and online communities2.

Sentiment and emotion in financial journalism: a corpus-based, cross-linguistic analysis of the effects of COVID

Despite the author’s conclusion, the recommendation does not hold true when comparing the performance of Amharic sentiment analysis model constructed in this study using deep learning with machine learning model proposed by Refs.6, 18. Findings from this study show deep learning models bring improvement compared to traditional machine learning in terms of work needed for feature extraction, performance, and scalability. Manual feature engineering wasn’t used for this work; so, it eliminates extra effort that was needed for feature extraction and in addition, the models could understand the context of a given sentence. When considering the model’s performance, a small (+ 1%) but significant increase was achieved. Scalability is the main challenge for standard machine learning models while the deep learning models used in this research showed that the accuracy for the model increases as the size of the dataset for training and testing increases.

In16, the authors worked on the BERT model to identify Arabic offensive language. The findings show that transfer learning is used across individual datasets from different sources and themes, such as YouTube comments from musician’s channels and Aljazeera News comments from political stories, yields unsatisfactory results. Overall, the results of the experiments show that need of generating new strategies for pre-training the BERT model for Arabic offensive language identification. Figure 13 shows, the performance of the four models for Amharic sentiment dataset, and when comparing their performance CNN-BI-LSTM showed a much better accuracy, precision, and recall. CNN-Bi-LSTM uses the capability of both models to classify the dataset, which is CNN that is well recognized for feature selection, while Bi-LSTM enables the model to include the context by providing past and future sequences.

Data and methods

An important early work by Tetlock (2007) explores possible correlations between the media and the stock market using information from the Wall Street Journal and finds that high pessimism causes downward pressure on market prices. A year later, Tetlock et al. (2008) deployed a bag-of-words model to assess whether company financial news can predict a company’s accounting earnings and stock returns. The results indicate that negative words in company-specific news predict low firm earnings, although market prices tend to under-react to the information entrenched in negative words. One significant challenge in translating foreign language text for sentiment analysis involves incorporating slang or colloquial language, which can perplex both translation tools and human translators46. Slang and colloquial languages exhibit considerable variations across regions and languages, rendering their accurate translation into a base language, such as English, challenging.

CNN models use convolutional layers and pooling layers to extract features, whereas Bidirectional-LSTM models preserve long-term dependencies between word sequences22. Hence CNN-Bidirectional-LSTM models are more suitable for sentiment classification. In order to visually compare the performance of each comparative model, this paper, based on Table 3, draws Fig. 7 (performance statistics of mainstream baseline model for sentiment analysis), Fig.

In the rest of this section, we review related work from the orthogonal perspectives of sentence-level sentiment analysis and gradual machine learning. The ablation study results reveal several important insights about the contributions of various components to the performance of our model. Firstly, it is evident that the complete model configuration comprising refinement processes, syntactic features, and the integration of the MLEGCN and attention modules-consistently yields the highest F1 scores across both the Res14 and Lap14 datasets. This underscores the synergy between the components, suggesting that each plays a crucial role in the model’s ability to effectively process and analyze linguistic data. Particularly, the removal of the refinement process results in a uniform decrease in performance across all model variations and datasets, albeit relatively slight.

The batch size was increased from 64 to 100, and the epoch number was decreased from 10 to 9. Change is made based on manual tunning and the experimental result is presented in Table 5. Four experiments were conducted by dividing the preprocessed dataset into three subsets which was 4000 sentences for training, 500 for validation, and another 500 for testing.

The majority of previous research papers47 focused on various areas of language processing such as stemming, stop word recognition and removal, and Urdu word segmentation and normalization. Similarly, in work44, the comparison of NB versus SVM for the language preprocessing steps of Urdu documents reveals that SVM performs better than NB regarding accuracy. Additionally, normalized term frequency gives much improved results for feature selection. The major drawback of the proposed system is that the tokenization is done based on punctuation marks and white spaces. However, due to the grammatical structure of the Urdu language, the writer may put white space between a single word such as (Khoubsorat, beautiful), which will cause the tokenizer to tokenize the single word as two words (khoub) and (sorat), which is incorrect.

• The experimental results align well with our existing knowledge and relevant statistical data, indicating the effectiveness of embedding methods in capturing the characteristics of media bias.
• For identifying sentiments and offensive language different pretrained models like logistic regression, CNN, Bi-LSTM, BERT, RoBERTa and Adapter-BERT are used.
• Use the data from social sentiment analytics to understand the emotional tone and preferences of your audience.
• Polynomial modeling and least square methods are adopted to define customer satisfaction and function implementation of customer requirements.

Subscores for the Sequencing Task and the Questionnaire and a total score for global ToM abilities were derived. Finally, functioning was measured using the QLS74, from which a subscore for each of the three subscales (i.e., Interpersonal Relations, Instrumental Role, and Personal Autonomy) and a total score were calculated. Neurocognition, social cognition, and functioning were assessed by trained clinical psychologists. In this article, I will cover the topic of Sentiment Analysis and how to implement a Deep Learning model that can recognize and classify human emotions in Netflix reviews. Analyzing sentiments of user conversations can give you an idea about overall brand perceptions.

The tool assigns individual scores to all the words, and a final sentiment is calculated. Sentiment analysis tools determine the positive-negative polarity of user-generated text at their most basic level, and offer more advanced tools for working with larger datasets. what is semantic analysis The best sentiment analysis tools ensure accuracy in analyzing textual data and identify subtle emotions, sarcasm, and how a sentiment relates to the data. There are four key features to consider when selecting a sentiment analysis tool for your business.

This is why I say it is naive to look at one factor such as sentiment and say that’s the reason a site is ranking. Just because you see a correlation does not mean it’s the reason a site is ranking. Information gain can be understood by using NLP processing to extract entities and knowledge about them, and that can lead to a determination of information gain.

We aim to explore how the economic upheaval of the latter period was conveyed in these publications and investigate the changes in sentiment and emotion in their language compared to the previous timeframe. To this end, we compiled comparable corpora of news items from two respected financial newspapers (The Economist and Expansión), covering both the pre-COVID and pandemic periods. Our corpus-based, contrastive EN-ES analysis of lexically polarized words and emotions allows us to describe the publications’ positioning in the two periods. We further filter lexical items using the CNN Business Fear and Greed Index, as fear and greed are the opposing emotional states most often linked to financial market unpredictability and volatility.

Best Python Libraries for Sentiment Analysis

The startup’s virtual assistant engages with customers over multiple channels and devices as well as handles various languages. Besides, its conversational AI uses predictive behavior analytics to track user intent and identifies specific personas. This enables businesses to better understand their customers and personalize product or service offerings. In layman’s terms, semantic search seeks to understand natural language the way a human would.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Once a sentence’s translation is done, the sentence’s sentiment is analyzed, and output is provided. However, the sentences are initially translated to train the model, ChatGPT App and then the sentiment analysis task is performed. The work described in12 focuses on scrutinizing the preservation of sentiment through machine translation processes.

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. 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.

Annotator bias and language ambiguity can all influence the sentiment labels assigned to YouTube comments, resulting in inconsistencies and uncertainties in the study. Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation.

On the other hand, ensemble learning methods can enhance the classification efficacy of imbalanced data by combining a series of weak classifiers32,33. Indicators including Precision, Recall and F1 are often applied to evaluate the classifier performance for imbalanced data. In this paper, the text data transformed from VPA data is segmented with natural sentences as the unit and then input into the established BERT deep transfer model. The functional, behavioral and structural customer requirements are classified by fine-tuning the BERT deep transfer model and classifier efficacy for imbalanced text data is evaluated. The neural network and machine learning methods without using pre-trained models performed the worst, with the overall performance far lower than the methods using pre-trained models. Among them, the SVM model performed relatively well, with the accuracy, recall and F1 values all exceeding 88.50%.

We illustrate the efficacy of GML by the examples from CR as shown in Table 5 and Figure 7. On \(t_1\), both GML and the deep learning model give the correct label; however, on all the other examples, GML gives the correct labels while the deep learning model mispredicts. In Figure 7, the four subfigures show the constructed factor subgraphs of the examples respectively. It can be observed that \(t_2\) has three relational factors, two of which are correctly predicted while the remaining one is mispredicted. However, GML still correctly predicts the label of \(t_2\) because the majority of its relational counterparts indicate a positive polarity. It is noteworthy that GML labels these examples in the order of \(t_1\), \(t_2\), \(t_3\) and \(t_4\).

And, since sentiment is often shared through online platforms like ecommerce sites, social media, and digital accounts, you can use those channels to access a deeper, almost intuitive understanding of customer desires and behaviors. Machine learning models such as reinforcement learning, transfer learning, and language transformers drive the increasing implementation of NLP systems. Text summarization, semantic search, and multilingual language models expand the use cases of NLP into academics, content creation, and so on. The cost and resource-efficient development of NLP solutions is also a necessary requirement to increase their adoption.

Their model enhances LSTM-derived contexts with syntax-aware weights, effectively distinguishing sentiment for multiple aspects and improving the overall accuracy of sentiment predictions70. Huang and Li’s work enhances aspect-level sentiment classification by integrating syntactic structure and pre-trained language model knowledge. Employing a graph attention network on dependency trees alongside BERT’s subword features, their approach achieves refined context-aspect interactions, leading to more precise sentiment polarity determinations in complex sentences71. Xu, Pang, Wu, Cai, and Peng’s research focuses on leveraging comprehensive syntactic structures to improve aspect-level sentiment analysis.

Zhao et al. address the challenge of extracting aspect-opinion pairs in ABSA by introducing an end-to-end Pair-wise Aspect and Opinion Terms Extraction (PAOTE) method. Their extensive testing indicates that this model sets a new benchmark, surpassing previous state-of-the-art methods52,53. This study investigated the effectiveness of using different machine translation and sentiment analysis models to analyze sentiments in four foreign languages. ChatGPT Our results indicate that machine translation and sentiment analysis models can accurately analyze sentiment in foreign languages. Specifically, Google Translate and the proposed ensemble model performed the best in terms of precision, recall, and F1 score. Furthermore, our results suggest that using a base language (English in this case) for sentiment analysis after translation can effectively analyze sentiment in foreign languages.