Amharic political sentiment analysis using deep learning approaches Scientific Reports

It uses AI and natural language processing to analyze customer reviews, surveys, and other forms of feedback to understand overall sentiment toward your brand. Meltwater’s latest sentiment analysis model incorporates features such as attention mechanisms, sentence-based embeddings, sentiment override, and more robust reporting tools. With these upgraded features, you can access the highest accuracy scores in the field of natural language processing. To examine more in depth the outcome of the linguistic analysis, it is important to comment on the specific clusters of linguistic features that emerged from the automatic classification.

Getting closer to the original vision of a web of connected data will require a combination of better structure, better tools and a chain of trust. In 2021 I and some colleagues published a research article on how to employ sentiment analysis on a applied scenario. In this article — presented at the Second ACM International Conference on AI in Finance (ICAIF’21) — we proposed an efficient way to incorporate market sentiment into a reinforcement learning architecture. The source code for the implementation of this architecture is available here, and a part of it’s overall design is displayed below.

Applying the NRC word–emotion association Lexicon

Noteworthy studies include Shen et al.13 for IMDB movie reviews, Zhou et al.14 for Chinese product reviews, Alharbi15 for Arabic datasets, and Ref.16 for Afaan Oromo datasets. Meena et al.17, proposes an effective sentiment analysis model using deep learning, particularly the CNN strategy, to evaluate customer sentiment from online product reviews. The findings suggest the potential for using online reviews to inform future product selections. While the study focused on laptops, phones, and televisions, there’s room for extending this approach to different products and languages in future research.

Just for the purpose of visualisation and EDA of our decomposed data, let’s fit our LSA object (which in Sklearn is the TruncatedSVD class) to our train data and specifying only 20 components. Where there would be originally r number of u vectors; 5 singular values and n number of 𝑣-transpose vectors. Previously we had the tall U, the square Σ and the long 𝑉-transpose matrices. Or, if we don’t do the full sum but only complete it partially, we get the truncated version.

According to their findings, CNN with several filters (3,4,5) outperformed the competition, whereas BiLSTM outperformed CLSTM and LSTM. The authors of47 used a single layer CNN with several filters to classify documents at the document level, and the results outperformed the baseline approaches. For document classification48, compared the performance of hybrid, machine learning, and deep learning models.

You can monitor and organize your social mentions or hashtags in real-time and track the overall sentiment towards your brand across various social media platforms like X, Facebook, Instagram, LinkedIn and YouTube. You can also monitor review sites such as Google Reviews, Yelp and TripAdvisor, and online communities and forums like Reddit and Quora. Regularly analyzing sentiment data helps you track your brand’s health over time. Identify trends in positive, negative‌ and neutral mentions to understand how your brand perception evolves. This ongoing monitoring helps you maintain a positive brand image and quickly address any issues. Sprout provides visual representations of sentiment trends, making it easier to spot shifts in public perception.

Sentiment analysis has been extensively studied at different granularities (e.g., document-level, sentence-level and aspect-level) in the literature. At the document level, the goal is to detect the sentiment polarity of an entire review, which may be composed of multiple sentences. Sentence-level sentiment analysis aims to detect the general polarity expressed in a single sentence. Representing the finest granularity, aspect-level sentiment analysis needs to identify the polarity expressed towards certain aspects of entity within a sentence. It is noteworthy that a sentence may express conflicting polarities towards difference aspects in a sentence. The state-of-the-art solutions for sentiment analysis at different granularities have been built upon DNN models.

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Next, I will choose two sets of words that hold positive and negative sentiments expressed commonly in the movie review context. Then, to predict the sentiment of a review, we will calculate the text’s similarity in the word embedding space to these positive and what is semantic analysis negative sets and see which sentiment the text is closest to. These are compiled of news items from two prestigious financial periodicals, The Economist and Expansión, and thus represent the situation a decade after the 2008 crisis and during the COVID crisis.

• The positive, negative, and neutral scores are ratios for the proportions of text that fall in each category and should sum to 1.
• Don’t neglect the insights from loyal customers who mean the most to your business.
• Manual data labeling takes a lot of unnecessary time and effort away from employees and requires a unique skill set.
• Our causality testing exhibited no reliable causality between the sentiment scores and the FTSE100 return with any lags.
• Which is a multilingual language model built upon the XLM-R architecture but with some modifications.

For instance, if the negative sentiment at a given day t increases, the volatility of the market would also increase the next day. The most commonly-used method for topic modeling, or topic discovery from a large number of documents, is Latent Dirichlet allocation (LDA). LDA is a generative topic model which generates combination of latent topics from a collection of documents, where each combination of topics produces words from the collection’s vocabulary with certain probabilities. A distribution on topics is first sampled from a Dirichlet distribution, and a topic is further chosen based on this distribution. Moreover, each document is modeled as a distribution over topics, and a topic is represented as a distribution over words. Another feature that made VADER the right tool for our experiments is that its sentiment analyzer can handle negations and UTF-8-encoded emojis, as well as acronyms, slang and punctuation.

To tackle these issues, natural language models are utilizing advanced machine learning (ML) to better understand unstructured voice and text data. This article provides an overview of the top global natural language processing trends in 2023. They range from virtual agents and sentiment analysis to semantic search and reinforcement learning. The primary purpose ChatGPT for using a set of machine learning algorithms with word and character n-gram features to establish baseline results against our proposed Urdu corpus. Our proposed dataset comprises with short and long type of user reviews that’s why we used various deep learning algroithms such GRU and LSTM to investigate the performance of algroithms against Urdu text.

To answer the first study question, the use of pre-trained word embeddings for sentiment analysis of Urdu language reviews is investigated. A deep learning model based on pre-trained word embedding captures long-term semantic relationships between words, unlike rule-based and machine learning-based approaches. To answer the second question, the deep learning models were compared to the machine learning-based methods and the rule-based method of Urdu sentiment analysis.

Identifying and categorizing opinions expressed in a piece of text (otherwise known as sentiment analysis) is one of the most performed tasks in NLP. Arabic, despite being one of the most spoken languages of the world, receives little attention as regards sentiment analysis. Therefore this article is dedicated to the implementation of Arabic Sentiment Analysis (ASA) using Python. This section explains the results of various experiments that have been executed in this study, the usefulness of our proposed architecture for Urdu SA, and the discussion of revealed results. In the evaluation of various implemented machine learning, deep learning, and rule-based algorithms, it is observed that the mBERT algorithm perform better than all other models. To implement Urdu SA, we need an annotated corpus containing user comments with their sentiments.

However, by implanting an adaptive mechanism, the system’s accuracy could be increased. Another study42 used a corpus collected from the BBC Urdu news website to work on Urdu text classification. Two types of filters were successfully implemented to collect the required data. A HTML parser is used to parse the obtained data, which yielded 500 news stories with 700 sentences containing the keywords mentioned above. Nearly 6000 sentences not annotated with emotions were discarded from those 500 news articles.

I selected a few sentences with the most noticeable particularities between the Gold-Standard (human scores) and ChatGPT. Then, I used the same threshold established previously to convert the numerical scores into sentiment labels (0.016). Thus, I investigated the discrepancies and gave my ruling, to which either Humans or the Chatgpt I found was more precise. The final result is displayed in the plot below, which shows how the accuracy (y-axis) changes for both models when categorizing the numeric Gold-Standard dataset, as the threshold (x-axis) is adjusted. Also, the training and testing sets are on the left and right sides, respectively.

Figures 14 and 15 show the changes in values when we compare the two periods in the Spanish and English periodicals, respectively. The columns in red represent decreasing trends taking place in the periods; the blue columns represent increasing trends. Newspaper articles and financial reports are key sources of information for investors in making decisions on investments, forming financial policies, and so on (Shalini, 2014, p. 270). Now that I have identified that the zero-shot classification model is a better fit for my needs, I will walk through how to apply the model to a dataset. These types of models are best used when you are looking to get a general pulse on the sentiment—whether the text is leaning positively or negatively. A Roman Urdu corpus has been created, contains 10,021 user comments belonging to various domains such as politics, sports, food and recipes, software, and movies.

Proponents claim that these mechanisms add the missing ingredients required for the Semantic Web to evolve from a platform for better searches to a more connected web of trusted data. Websites and third-party apps can use tagged data to automatically pull specific types of information from various sites into summary cards. For example, movie theaters can list showtimes, movie reviews, theater locations and discount pricing that shows up in searches. A website owner or content creator adds linked data tags according to standard search engine schemas, which makes it easier for search engines to automatically extract data about, for example, store hours, product types, addresses and third-party reviews. The Rotten Tomatoes website enhanced click-through by 25% when it added structured data. In this sense, even though ChatGPT outperformed the domain-specific model, the ultimate comparison would need fine-tuning ChatGPT for a domain-specific task.

Translation to base language: English

Offensive language is any text that contains specific types of improper language, such as insults, threats, or foul phrases. This problem has prompted various researchers to work on spotting inappropriate communication on social media sites in order to filter data and encourage positivism. The earlier seeks to identify ‘exploitative’ sentences, which are regarded as a kind of degradation6. The proposed model achieved 91.60% which is 6.81%, 6.33%, and 2.61% improvement from CNN, Bi-LSTM, and GRU respectively. Mostly in this research work, overfitting was encountered but different hyperparameters were applied to control the learning process. Hyperparameters like Learning rate, dropout, Momentum, and random state for our case shifted the model from overfitting to a good fit.

However, when two languages are mixed, the data contains elements of each in a structurally intelligible way. Because code-mixed information does not belong to a single language and is frequently written in Roman script, typical sentiment analysis methods cannot be used to determine its polarity3. In the CNN experimentation, we began by inputting the preprocessed data into the CNN layer to facilitate feature extraction. The CNN layer employed 128 filters with 5 kernels and utilized the ReLU activation function. Following this feature extraction step, the data was forwarded to the GlobalMaxPooling1D layer, which downed sample the representation by selecting the maximum value across time, converting the output from 2 to 1D.

Its dashboard has a clean interface, with a sidebar displaying filters for selecting the samples used for sentiment analysis. Next to the sidebar is a section for visualization where you can use colorful charts and reports for monitoring sentiments by topic or duration and summarize them in a keyword cloud. Deep learning techniques, inspired by the brain’s structural and autonomous learning ability, streamline computational model development and outperform standard machine learning in sentiment analysis, making them crucial for managing user-generated data19. Moreover, the unstructured nature of YouTube comments presents challenges for analysis, but recurrent neural networks (RNNs) excel in sequence learning, capturing subtle sentiments and enhancing their value for platforms such as YouTube and social media11. Today, semantic analysis methods are extensively used by language translators. Earlier, tools such as Google translate were suitable for word-to-word translations.

Importantly, when we performed an additional fine-grained correlational analysis, different patterns of associations between linguistic features and cognitive aspects emerged for the two clusters. Conversely, in more preserved individuals language and cognitive abilities seem to be rather independent. As for social cognition, no significant associations were highlighted in the additional analysis. Our data align with such evidence of a relative interdependence between language profiling and sociocognitive skills.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. Then, benchmark sentiment performance against competitors and identify emerging threats. Continuous updates ensure the hybrid model improves over time, enhancing its ability to accurately reflect customer opinions. The Global Startup Heat Map below highlights the global distribution of the exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform, the Heat Map reveals that the US sees the most startup activity. Google cares about user satisfaction, and they are continuously fine-tuning their algorithm to understand better and satisfy searchers.

We will iterate through 10k samples for predict_proba make a single prediction at a time while scoring all 10k without iteration using the batch_predict_proa method. The id2label and label2id dictionaries has been incorporated into the configuration. We can retrieve these dictionaries from the model’s configuration during inference to find out the corresponding class labels for the predicted class ids. The DataLoader initializes a pretrained tokenizer and encodes the input sentences. We can get a single record from the DataLoader by using the __getitem__ function.

Foster stronger customer connections and build long-lasting relationships by engaging with them and solving issues promptly. Positive engagements, such as acknowledging compliments or sharing user-generated content, can further build brand recall and loyalty. Insights from social sentiment analytics can help you improve your brand recall and resonate better with your target audience. They also help you manage brand reputation and spot shifts in market sentiment so you can address them proactively. The Active Listeners tab provides one-click access to queries, including complaints, compliments and specific customer experiences. This feature helps you quickly identify and respond to various types of feedback, which gives you context on how to engage with your audience.

Moreover, the LSTM neurons are split into two directions, one for forward states and the other for backward states, to form bidirectional LSTM networks32. Therefore, Bidirectional LSTM networks use input from past and future time frames to minimize delays but require additional steps for backpropagation over time due to the noninteracting nature of the two directional neurons33. Semantic analysis is a method used in linguistics, computer science, and artificial intelligence ChatGPT App to understand the meaning of words and sentences in context. It examines relationships among words and phrases to comprehend the ideas and concepts they convey. In natural language processing, semantic analysis helps machines grasp the nuances of human language, such as irony, sarcasm, or ambiguity. It is a critical component of technologies that rely on language understanding, like text analysis, language translation, and voice recognition systems.

A sentiment analysis model can not notice this sentiment shift if it did not learn how to use contextual indications to predict sentiment intended by the author. To illustrate this point, let’s see review #46798, which has a minimum S3 in the high complexity group. Starting with the word „Wow“ which is the exclamation of surprise, often used to express astonishment or admiration, the review seems to be positive.

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. Urdu, on the other hand, is a resource-poor language with a severe lack of sentiment lexicon. Problems with Urdu word segmentation, morphological structure and vocabulary variances are among the main deterrents to developing a fully effective Urdu sentiment analysis model. The one subsection describes the research situation of customer requirements classification, and another subsection introduces the deep transfer learning in the natural language processing, and a third subsection elaborates the customer requirements mining.

The classification task involves two-class polarity detection (positive-negative), with the neutral class excluded. Encouraging outcomes are achieved in polarity detection experiments, notably by utilizing general-purpose classifiers trained on translated corpora. However, it is underscored that the discrepancies between corpora in different languages warrant further investigation to facilitate more seamless resource integration. One common and effective type of sentiment classification algorithm is support vector machines.

It’s easier to see the merits if we specify a number of documents and topics. Suppose we had 100 articles and 10,000 different terms (just think of how many unique words there would be all those articles, from “amendment” to “zealous”!). When we start to break our data down into the 3 components, we can actually choose the number of topics — we could choose to have 10,000 different topics, if we genuinely thought that was reasonable. However, we could probably represent the data with far fewer topics, let’s say the 3 we originally talked about.

• In 2021 I and some colleagues published a research article on how to employ sentiment analysis on a applied scenario.
• Luckily the dataset they provide for the competition is available to download.
• Generally, the results of this paper show that the hybrid of bidirectional RNN(BiLSTM) and CNN has achieved better accuracy than the corresponding simple RNN and bidirectional algorithms.
• 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.

The main befits of such language processors are the time savings in deconstructing a document and the increase in productivity from quick data summarization. ChatGPT is a GPT (Generative Pre-trained Transformer) machine learning (ML) tool that has surprised the world. Its breathtaking capabilities impress casual users, professionals, researchers, and even its own creators. Moreover, its capacity to be an ML model trained for general tasks and perform very well in domain-specific situations is impressive. I am a researcher, and its ability to do sentiment analysis (SA) interests me.

Based on such consistency, we can naturally apply Semantic Differential to measure a media outlet’s attitudes towards different entities and concepts, i.e., media bias. Then we’ll end up with either more or fewer samples of majority class than minority class depending on n neighbours we set. For example, with my dataset, if I run NearMiss-3 with default n_neighbors_ver3 of 3, it will complain and the number of neutral class(which is majority class in my dataset) will be smaller than negative class(which is minority class in my dataset). So I explicitly set n_neighbors_ver3 to be 4, so that I’ll have enough majority class data at least the same number as the minority class. Compared to the model built with original imbalanced data, now the model behaves in opposite way. The precisions for the negative class are around 47~49%, but the recalls are way higher at 64~67%.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Here, we highlight some of the issues to remind readers to use it more cautiously. Above all, while GDELT provides a vast amount of data from various sources, it cannot capture every event accurately. It relies on automated data collection methods, and this could result in certain events being missed.

With the aim of measuring sentiment, we conducted a preliminary analysis of sentiment in the two smaller (pre-COVID) corpora, which comprised fewer than one million words in each language (cf. Table 4). This lower number of words was necessary due to the limitations of the Lingmotif 2 softwareFootnote 6 (Moreno-Ortiz, 2021). Its basic function is to determine the semantic orientation of a text, that is, the extent to which it can be said to be positive or negative, by detecting the positivity or negativity contained in the different linguistic expressions in the text(s) analysed.