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10 results for “Movie reviews”
MOBO: The MOvie and BOok reviews dataset
<p>The <strong>MOBO</strong> dataset.</p> <p>The <em><strong>MOvie and BOok reviews dataset</strong></em> is a collection made up of movie and book reviews, paired with their related plots.<br> The reviews come from different publicly available datasets: the Stanford's IMDB movie reviews [1], the GoodReads [2] and the Amazon reviews dataset [3]. With the help of 15 annotators, we further labeled more than 18,000 reviews' sentences (~6000 per corpus), marking the sentence polarity (<em>Positive</em>, <em>Negative</em>), or whether a sentence describes its corresponding movie/book <em>Plot</em>, or none of the above (<em>None</em>). In the <code>dataset</code> folder, we have shared an excerpt of the annotated sentences for each dataset.</p>
Large Movie Review Dataset
<p>IMDB dataset having 50K movie reviews for natural language processing or Text analytics.<br> This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.<br> For more dataset information, please go through the following link,<br> <a href="http://ai.stanford.edu/~amaas/data/sentiment/">http://ai.stanford.edu/~amaas/data/sentiment/</a>.</p>
Sentiment analysis in Galaxy with IMDB movie review dataset
<p>IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org). </p>
Gujarati Movie Reviews with Tagged Sentiments (Positive/ Negative/ Neutral)
<p>This dataset encompasses around 500 entries of movie review descriptions written in the Gujarati language. Each entry is paired with a sentiment classification tag. The first column contains the actual movie review descriptions, while the second column contains sentiment tags with the following meanings:</p><ul><li>"0" denotes that the corresponding review expresses a negative sentiment.</li><li>"1" signifies a neutral sentiment.</li><li>"2" conveys a positive sentiment.</li></ul><p>To sum it up succinctly, this dataset provides a valuable collection of <strong>Gujarati movie reviews</strong>, each thoughtfully categorized as either <strong>negative</strong>, <strong>neutral</strong>, or <strong>positive</strong> in tone, offering rich insights for sentiment analysis tasks and research.</p><p>The dataset is manually tagged by native speakers with more than 20 years of experience in using the language.</p>
Movie review (MR)
<p>Movie review (MR) dataset is a binary (positive and negative labels) sentiment polarity composed of movie reviews.</p> <p>The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition.</p>
Short Online Reviews, and Movie Information from Douban(covering 224 movies released in China)
<p>This dataset contains 3 part:</p> <table> <tbody> <tr> <th>Filename (3 files)</th> <th>Size</th> <th>Description </th> </tr> <tr> <td>douban.movies.json</td> <td>147 kB</td> <td>Movie information from Douban, including a movie's Douban ID, name, rating, staff list, release time, type, box office result, ratings, and amount of official reports mentioned it.</td> </tr> <tr> <td>index.json</td> <td>123 kB</td> <td>The index mentioned in the paper.</td> </tr> <tr> <td>original_comments.json</td> <td>46 MB</td> <td>Original Chinese reviews, including release time and "useful" count until Sept, 2022.</td> </tr> </tbody> </table>
Personal movie reviews from IMDB platform
<p>The dataset contains basic information about movies and reviews published on IMDB platform. The dataset contains the following information.</p> <ul> <li>Basic information about movies.</li> <li>Genres from the previous movies.</li> <li>A list of user reviews containing 1000 reviews per movie.</li> <li>Metadata about the extraction process.</li> </ul> <p>The dataset consists of a series of csv files. The semicolon is used as a separator.</p> <p>The exact data is restricted to three specific movies.</p> <ol> <li>The Godfather</li> <li>The Godfather: Part II.</li> <li>The Godfather. Part III.</li> </ol> <p>The data is owned by IDMB and it is published under the CC BY-NC-SA 4.0 license. You may not share or modify this date without giving credit to the original owners. The data cannot be used under any commercial purposes. This dataset can only be used with academic purposes.</p>
Movie Reviews Dataset
<p>This dataset, derived from professional movie critic reviews (https://www.cs.cornell.edu/people/pabo/movie-review-data/), includes 17,165 reviews across 2,544 unique movies spanning from the 1940s to the 2020s. We enriched the dataset with metadata such as the actor, director, and writer (including their genders) using the OMDb API, gender identifier, as well as additional validation methods.</p>
Red in tooth and claw: A review of animal antagonistic roles in movies
<p>1. Although cinema narrative represents a fundamental communication tool in framing public opinion, whether or not a negative representation of certain species in the context of animal-horror movies might increase the attitudinal hostilities toward them, remains an aspect of wildlife perception that is poorly studied.</p> <p>2. Here, we reviewed online sources from the last 70 years to describe the negative representation of animals roles in horror and disaster movies. Specifically, we described species diversity, how the animal was depicted, the cause for its aggressive behavior, and how it came in contact with the human characters. By means of Principal Coordinate Analysis (PCoA), we also highlighted three main typologies of animal-horror movies.</p> <p>3. The dataset consisted of 263 titles produced worldwide from 1950 to 2019. The results showed that animal representation is transversal yet uneven, with five species groups out of 18 appearing in more than half of the movies. There were significant associations between species, their representation and the different kinds of movies they appeared in, with some species groups appearing more commonly in certain types of film plots rather than others.</p> <p>4. Together, the results suggested that both the themes and topics of animal-horror movies were often the result of a combination of factors, including fashion-driven audience interests, societal and political concerns, and technological availability at the time of production. Whether this repeated and variegated representation can increase attitudinal hostility, remains however unclear.</p>
Red in tooth and claw: A review of animal antagonistic roles in movies
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