With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after ...With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biolog...Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biological community, water and sediment quality of the lakes due to water level changes is a reflection of the geology of the area and the anthropogenic activities in the watershed. The study conducted between January 2018 and December 2019 was to explore relationships between the phosphorous input and Water Level Fluctuations (WLF) recorded by Water Resource Authority (WRA). Lake water samples were analyzed in the laboratory for phosphorous using molybdenum blue-ascorbic method and recorded using spectrophotometer. Chlorophyll-<em>a</em> was determined by extracting a filtered sample with 15 ml acetone and incubating overnight and thereafter read using a double beam spectrophotometer. Total Suspended Solids (TSS) was determined by filtering 200 ml of a water sample and dried overnight at 105<span style="white-space:nowrap;">°</span>C. The lowest and highest phosphorous concentrations recorded were 0.2 mg/l and 0.42 mg/l at NST7 and NST2, respectively. Measurements of Chlorophyll-<em>a</em> were 0.32 mg/l and 0.42 mg/l at NST9 and NST2, respectively. Secchi transparency measurements were 32.9 cm at NST3 and 84 cm at NST1. The highest and lowest TSS concentrations were 0.14 mg/l and 0.13 mg/l at NTS1 and NST8, respectively. The hydrodynamic regime in most tropical lakes plays a significant role in the re-reaction of phosphorous that consequently influences productivity. Tropical lakes have extreme lake level fluctuations which accelerate the production process. The influence of water level changes on aquatic productivity is crucial in most tropical lakes and should be taken into consideration when assessing the environmental impacts.展开更多
A systematic experimental investigation to understand the effect of heat loss and the thermoelectric aspect ratio (cross sectional area and length) on a flat plate solar thermoelectric system performance was carried o...A systematic experimental investigation to understand the effect of heat loss and the thermoelectric aspect ratio (cross sectional area and length) on a flat plate solar thermoelectric system performance was carried out. The investigation involved a series of experiments on systems with 4 different sizes of thermoelectric generators, and it was tested in 5 different vacuum levels during the steady-state. The detailed experimental investigation provided a substantial amount of data, which revealed that the system performance of both heat and electricity power were improved when the heat lost was minimised. The system’s performance strongly depended on the aspect ratio of the thermoelectric generators. This finding might have a significant impact on the cost of the system by saving the user’s and the manufacturer’s time in examining different TEGs with different aspect ratios in order to get the optimum size optimisation of the hybrid system, as well as reduce the manufacturing cost.展开更多
文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
文摘Hydrological dynamics affect water levels and thus affecting ecosystem structure and functions. Lake levels in tropical ecosystems affect phosphorous input through runoff from adjacent watersheds. The resultant biological community, water and sediment quality of the lakes due to water level changes is a reflection of the geology of the area and the anthropogenic activities in the watershed. The study conducted between January 2018 and December 2019 was to explore relationships between the phosphorous input and Water Level Fluctuations (WLF) recorded by Water Resource Authority (WRA). Lake water samples were analyzed in the laboratory for phosphorous using molybdenum blue-ascorbic method and recorded using spectrophotometer. Chlorophyll-<em>a</em> was determined by extracting a filtered sample with 15 ml acetone and incubating overnight and thereafter read using a double beam spectrophotometer. Total Suspended Solids (TSS) was determined by filtering 200 ml of a water sample and dried overnight at 105<span style="white-space:nowrap;">°</span>C. The lowest and highest phosphorous concentrations recorded were 0.2 mg/l and 0.42 mg/l at NST7 and NST2, respectively. Measurements of Chlorophyll-<em>a</em> were 0.32 mg/l and 0.42 mg/l at NST9 and NST2, respectively. Secchi transparency measurements were 32.9 cm at NST3 and 84 cm at NST1. The highest and lowest TSS concentrations were 0.14 mg/l and 0.13 mg/l at NTS1 and NST8, respectively. The hydrodynamic regime in most tropical lakes plays a significant role in the re-reaction of phosphorous that consequently influences productivity. Tropical lakes have extreme lake level fluctuations which accelerate the production process. The influence of water level changes on aquatic productivity is crucial in most tropical lakes and should be taken into consideration when assessing the environmental impacts.
文摘A systematic experimental investigation to understand the effect of heat loss and the thermoelectric aspect ratio (cross sectional area and length) on a flat plate solar thermoelectric system performance was carried out. The investigation involved a series of experiments on systems with 4 different sizes of thermoelectric generators, and it was tested in 5 different vacuum levels during the steady-state. The detailed experimental investigation provided a substantial amount of data, which revealed that the system performance of both heat and electricity power were improved when the heat lost was minimised. The system’s performance strongly depended on the aspect ratio of the thermoelectric generators. This finding might have a significant impact on the cost of the system by saving the user’s and the manufacturer’s time in examining different TEGs with different aspect ratios in order to get the optimum size optimisation of the hybrid system, as well as reduce the manufacturing cost.
