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59 results for “product reviews”
Word Embedding of Amazon Product Review Corpus
<p>A word embedding of the <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets">Amazon Product Review Corpus</a> (<a href="https://www.doi.org/10.1145/1341531.1341560">Jindal and Liu, 2008</a>).</p> <p>Created using <a href="https://code.google.com/archive/p/word2vec/">Word2Vec</a> in CBOW mode, 500 dimensions and window size 5.</p> <p>Words have been lemmatised and particle verbs have been merged into a single token (e.g. <code>calm_down</code>).</p> <ul> </ul> <p> </p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). <strong>"Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features"</strong>. Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Amazon product reviews (mock dataset)
<p>About</p> <p>This is a mock dataset with Amazon product reviews. Classes are structured: 6 "level 1" classes, 64 "level 2" classes, and 510 "level 3" classes.</p> <p><br> 3 files are shared:</p> <ul> <li>train_40k.csv - training 40k Amazon product reviews</li> <li>valid_10k.csv - 10k reviews left for validation</li> <li>unlabeled_150k.csv - raw 150k Amazon product reviews, these can be used for language model finetuning.</li> </ul> <p>Level 1 classes are: health personal care, toys games, beauty, pet supplies, baby products, and grocery gourmet food.</p> <p>Dataset originally from <a href="https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification">https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification</a></p>
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop.
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972).
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977].
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption.
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь.
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980].
Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors.
Рис. 7. Сумма температур перед нерестом приморского гребешка (а) и количество спата на коллекторах (б). Fig. 7. The sum of temperatures before spawning of the Japanese scallop (a) and the number of spat on collectors (б). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 7. Сумма температур перед нерестом приморского гребешка (а) и количество спата на коллекторах (б). Fig. 7. The sum of temperatures before spawning of the Japanese scallop (a) and the number of spat on collectors (б).
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личи- нок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личи- нок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3).
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхно- сти: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхно- сти: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС).
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста.
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Dataset Literature Review Online Business and Products
<p>Data ini diigunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "online business" dan "products" </p>
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