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International audience ; Temporal dimension contains important information for sentiment analysis of microblog data such as tweets. Previous works on sentiment visualization could not address the multidimensional nature of sentiment together with temporal information. In this work, we introduce SentimentClock for visualizing the sentiment of time-varying Twitter data on 2D affective space. Our visualization enables various interesting tasks : (1) Visualize and compare temporal variations of sentiments. (2) Compare sentiments variations of tweets on different topics. (3) Visualize the distribution of tweets on 2D affective space. (4) Visualize both dimensions of sentiments (i.e. valence, arousal) and their semantic meanings (e.g. elated, stressed). Fig.1 SentimentClock of the tweets collected on 2013 Australian election day (7-Sep-2013) Fig.2 SentimentClocks of tweets on two different topics: Australian Politics (left) and World Cup 2014 (right) Fig.1 shows the sentiment visualization of 36016 related tweets posted on 2013 Australian election day. In the evening (18:00 to 22:00), which is the vote counting and result releasing period, tweets are found to have both high arousal and valence, primarily falling into the elated and excited range with high strength. Fig.2 shows the sentiment visualization of 71200 tweets on two topics. Tweets on the topic " Australian Politics " are more spread out along the sentiment wheel and express more negative sentiments, e.g. upset and stressed. However, tweets on the topic " World cup 2014 " are mainly concentrated within the range of content and elated.

International audience ; Temporal dimension contains important information for sentiment analysis of microblog data such as tweets. Previous works on sentiment visualization could not address the multidimensional nature of sentiment together with temporal information. In this work, we introduce SentimentClock for visualizing the sentiment of time-varying Twitter data on 2D affective space. Our visualization enables various interesting tasks : (1) Visualize and compare temporal variations of sentiments. (2) Compare sentiments variations of tweets on different topics. (3) Visualize the distribution of tweets on 2D affective space. (4) Visualize both dimensions of sentiments (i.e. valence, arousal) and their semantic meanings (e.g. elated, stressed). Fig.1 SentimentClock of the tweets collected on 2013 Australian election day (7-Sep-2013) Fig.2 SentimentClocks of tweets on two different topics: Australian Politics (left) and World Cup 2014 (right) Fig.1 shows the sentiment visualization of 36016 related tweets posted on 2013 Australian election day. In the evening (18:00 to 22:00), which is the vote counting and result releasing period, tweets are found to have both high arousal and valence, primarily falling into the elated and excited range with high strength. Fig.2 shows the sentiment visualization of 71200 tweets on two topics. Tweets on the topic " Australian Politics " are more spread out along the sentiment wheel and express more negative sentiments, e.g. upset and stressed. However, tweets on the topic " World cup 2014 " are mainly concentrated within the range of content and elated.

International audience ; Temporal dimension contains important information for sentiment analysis of microblog data such as tweets. Previous works on sentiment visualization could not address the multidimensional nature of sentiment together with temporal information. In this work, we introduce SentimentClock for visualizing the sentiment of time-varying Twitter data on 2D affective space. Our visualization enables various interesting tasks : (1) Visualize and compare temporal variations of sentiments. (2) Compare sentiments variations of tweets on different topics. (3) Visualize the distribution of tweets on 2D affective space. (4) Visualize both dimensions of sentiments (i.e. valence, arousal) and their semantic meanings (e.g. elated, stressed). Fig.1 SentimentClock of the tweets collected on 2013 Australian election day (7-Sep-2013) Fig.2 SentimentClocks of tweets on two different topics: Australian Politics (left) and World Cup 2014 (right) Fig.1 shows the sentiment visualization of 36016 related tweets posted on 2013 Australian election day. In the evening (18:00 to 22:00), which is the vote counting and result releasing period, tweets are found to have both high arousal and valence, primarily falling into the elated and excited range with high strength. Fig.2 shows the sentiment visualization of 71200 tweets on two topics. Tweets on the topic " Australian Politics " are more spread out along the sentiment wheel and express more negative sentiments, e.g. upset and stressed. However, tweets on the topic " World cup 2014 " are mainly concentrated within the range of content and elated.

International audience ; Temporal dimension contains important information for sentiment analysis of microblog data such as tweets. Previous works on sentiment visualization could not address the multidimensional nature of sentiment together with temporal information. In this work, we introduce SentimentClock for visualizing the sentiment of time-varying Twitter data on 2D affective space. Our visualization enables various interesting tasks : (1) Visualize and compare temporal variations of sentiments. (2) Compare sentiments variations of tweets on different topics. (3) Visualize the distribution of tweets on 2D affective space. (4) Visualize both dimensions of sentiments (i.e. valence, arousal) and their semantic meanings (e.g. elated, stressed). Fig.1 SentimentClock of the tweets collected on 2013 Australian election day (7-Sep-2013) Fig.2 SentimentClocks of tweets on two different topics: Australian Politics (left) and World Cup 2014 (right) Fig.1 shows the sentiment visualization of 36016 related tweets posted on 2013 Australian election day. In the evening (18:00 to 22:00), which is the vote counting and result releasing period, tweets are found to have both high arousal and valence, primarily falling into the elated and excited range with high strength. Fig.2 shows the sentiment visualization of 71200 tweets on two topics. Tweets on the topic " Australian Politics " are more spread out along the sentiment wheel and express more negative sentiments, e.g. upset and stressed. However, tweets on the topic " World cup 2014 " are mainly concentrated within the range of content and elated.

In this manuscript, which appeared in ALTEX 35 , 235-253 ( doi:10.14573/altex.1712182 ), the Acknowledgements should read: This work was supported by the Land BW, the Doerenkamp-Zbinden Foundation, the DFG (RTG1331, KoRS-CB), the BMBF (NeuriTox), and it has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 681002 (EU-ToxRisk).